Defining Hepatorenal Syndrome. Adopted from International Ascites Club and in [21, 22].
\r\n\tThis book will consist of chapters that are an elegant mix of reviews and current developments on the subject that will be useful both to an expert on the subject as well as a newcomer to this area of research.
",isbn:"978-1-83969-076-1",printIsbn:"978-1-83969-075-4",pdfIsbn:"978-1-83969-092-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"8a2fd9bbbbae283bf115881d9d5cc47a",bookSignature:"Dr. Ashim Kumar Dutta",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11857.jpg",keywords:"Frenkel Excitons, Wannier-Mott Excitons, Low Dimensional Solids, Molecular Crystals and Aggregates, Exciton Diffusion and Hopping, Exciton–Exciton Annihilation, Dynamics, Scaling Laws, Photoluminescence, Exciton Lifetime, Energy Harvesting, Semiconductors",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 19th 2022",dateEndSecondStepPublish:"May 17th 2022",dateEndThirdStepPublish:"July 16th 2022",dateEndFourthStepPublish:"October 4th 2022",dateEndFifthStepPublish:"December 3rd 2022",remainingDaysToSecondStep:"5 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Ashim Kumar Dutta received his Ph.D. in physical chemistry from the Indian Association for the Cultivation of Science (IACS). 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Earlier, he had worked with Unilever as a senior researcher and product development manager in their Home and Personal Care Category, with United Phosphorus Limited and Indofil as their global head for agrochemical formulations. He has authored/co-authored 36 articles in international journals and 19 patents. He received his Ph.D in physical chemistry from Indian Association for the Cultivation of Science (IACS) – a premiere research institute in India in 1993. Dr. Dutta has worked on various international post-doctoral fellowships in Japan, Canada and USA. 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In simple terms, HRS is defined as a relative rise in creatinine and relative drop in serum glomerular filtration rate (GFR) alongside renal plasma flow (RPF) in the absence of other competing etiologies of acute kidney injury (AKI) in patients with hepatic cirrhosis [1, 2, 3, 4, 5, 6, 7]. It represents the end stage complication of decompensated cirrhosis in the presence of severe portal hypertension, in the absence of prerenal azotemia, acute tubular necrosis or others. It is a diagnosis of exclusion [2]. The recognition of HRS is of paramount importance for clinicians as it carries a high mortality rate. Recent advances in understanding the pathophysiology of the disease improved treatment approaches, but the overall prognosis remains poor, with Type I HRS having an average survival under 2 weeks [3]. Generally speaking, AKI and renal failure in cirrhotic patients carry a very high mortality rate, with up to 60% mortality rate for patients with renal failure and cirrhosis and 86.6% of overall mortality rates of patients admitted to the intensive care unit [4, 5]. Of the various etiologies of renal failure in cirrhosis, HRS carries a poor prognosis among cirrhotic patients with AKI.
HRS continues to pose a diagnostic challenge. AKI is relatively frequent, seen in about 20% of patients with cirrhosis [8]. AKI can be either pre-renal, intrarenal or postrenal. Prerenal causes include hypovolemia, infection, use of vasodilators and functional due to decreased blood flow to the kidney, intra-renal such as glomerulopathy, acute tubular necrosis and post-renal such as obstruction. Patients with cirrhosis are susceptible to developing renal impairment. HRS may be classified as type 1 or rapidly progressive disease, and type 2 or slowly progressive disease. There are other types of HRS [9], but this chapter will focus on type 1 HRS and type 2 HRS. HRS is considered a functional etiology of AKI as there is an apparent lack of nephrological parenchymal damage. This is one of several possibilities of AKI in patients with both acute and chronic liver disease.
AKI is one of the most severe complications that could occur with cirrhosis. Up to 50% of hospitalized patients with cirrhosis can suffer from AKI, and as mentioned earlier an AKI in the presence of cirrhosis in a hospitalized patient has been associated with nearly a 3.5-fold increase in mortality [6].
The definition of HRS will be discussed in this chapter, but it is characterized specifically as a form of AKI that occurs in patients with advanced liver cirrhosis which results in a reduction in renal blood flow, unresponsive to fluids this occurs in the setting of portal hypertension and splanchnic vasodilation [7].
This chapter will discuss the incidence, definitions and management of HRS, focusing mainly on HRS type I and type II.
AKI is a common entity in cirrhotic patients at baseline. It is also commonly seen in general hospitalized patients, both with and without cirrhosis. This fundamentally means that a clinician should be able to distinguish various etiologies of AKI establish the reason for AKI in each cirrhotic patient so that management can be conducted appropriately.
As mentioned before, the frequency of AKI in patients with underlying liver pathology can be as high as 50%. One study looked at hospitalized patients with cirrhosis. Of these patients, 19% found to have an AKI, out of these 23% found to have HRS [10]. “The AKI was divided into pre-renal, intrinsic, and post-renal. Pre-renal injury was the most common form of AKI which represented 68% of patients with AKI. The pre-renal injury was usually volume responsive, while HRS is non-volume responsive. In most cases, the injury was volume responsive and therefore less likely HRS [11, 12]. Although HRS is not always the most common cause of renal impairment in cirrhosis; renal impairment itself is commonly seen as the frequency of AKI in cirrhosis can vary in the literature from approximately 15–40% [13, 14, 15].
The etiologies of AKI in cirrhosis vary, and the prognosis that each etiology carries also varies. One large prospective study found that hypovolemia and infections were in fact the most common culprits of AKI in cirrhosis, with HRS being identified in 13% of cases [16]. The definition of HRS is important as it can guide clinicians into decision making. For instance, if the etiology of an AKI in cirrhosis is reversible and will not cause significant long-term impairment, the urgency for immediate transplantation dissipates. Conversely, if there is the development of HRS, there may be urgent indication for transplantation.
While there are varying figures reported in the literature on the frequency of AKI in the cirrhotic population, it is evident that it is a common entity. Not all AKI in cirrhosis is considered HRS and defining HRS as the specific cause of renal impairment in cirrhosis represents another challenge for clinicians.
As stated previously, HRS is defined as renal impairment that occurs in patients who have clinically established cirrhosis or have significant liver impairment. The most widely used definition is the relative rise in creatinine and the relative drop in serum GFR and renal plasma flow in the absence of other causes of AKI like prerenal, renal or post-renal. Given its poor prognosis, HRS was formerly associated with the term terminal functional renal failure [17]. In theory, since there is no intrinsic kidney pathology, upon reversing the hepatic dysfunction either medically or via transplantation, there should be resolution of HRS. In intrinsic renal pathologies, this would not be the case. Before considering HRS, clinicians should rule out other competing etiologies.
Differentiating HRS from other etiologies of AKI in cirrhotic patients is clinically of high importance because of the pronounced difference in management and prognosis. Patients with liver cirrhosis are prone to have acute, subacute and chronic kidney disease through a variety of mechanisms. Clinicians should have a broad differential diagnosis when approaching patients with AKI as there is no definitive test for HRS yet [18]. It is therefore necessary to rule out other differential diagnosis before a diagnosis of HRS is made. Identification of risk factors and careful assessment of the renal system are the mainstay to make such a diagnosis.
Cirrhotic patients may have a certain level of renal insufficiency at baseline since some etiologies of cirrhosis can directly or indirectly lead to renal insufficiency. For instance, patients with non-alcoholic fatty liver disease have higher incidence of obesity and associated diabetes and diabetic nephropathy. Also, both glomerulonephritis and vasculitis can occur in patients with liver cirrhosis secondary to viral hepatitis [2]. These are just a few examples of how one pathology can affect both the hepatic and renal system.
Given the wide spectrum of possibilities, when approaching a renal impairment in a patient with cirrhosis, a systematic approach can be of benefit to clinicians to assess the nature of renal impairment. Causes of AKI and renal failure in cirrhotic patients can be summarized in four main categories.
This is usually due to hemorrhage related to gastrointestinal bleed or fluid loss associated with excessive diuresis or diarrhea induced by excessive laxatives use [19]. Also, can be secondary to different infectious etiologies including spontaneous bacterial peritonitis. In any of these cases, renal failure will occur soon after any of the mentioned hypovolemic events [16, 19]. Due to the fact that patients with worsening liver cirrhosis will have decreased intravascular volume and mean arterial resistance [17], hypovolemia should be considered as a frequent component of AKI in those patients [16]. The management of hypovolemia induced renal failure is to address the volume status.
By definition HRS is a purely functional disease and it does not induce renal parenchymal damage. However, any parenchymal renal disease can occur in both cirrhotic patients and non-cirrhotic patients. The presence of proteinuria, hematuria or both is associated with glomerular disease. Differentiating HRS from acute Tubular Necrosis (ATN) remains difficult. While the presence of muddy brown casts favors ATN, other urinary indexes like fractional excretion of sodium (FeNa) can be misleading due to the prolonged use of diuretics in cirrhotic patients. Granular casts can be seen in both ATN and HRS [19].
Drug-induced tubular/tubulointerstitial injury is a common cause of AKI especially with the consideration ill patients such as those with cirrhosis will inevitably need medications. There are various pathways and in which a drug can cause renal injury [20]. Some examples can include aminoglycosides, vancomycin, and even administration of contrast needed for imaging studies.
HRS is a diagnosis of exclusion based on the previously mentioned criteria. This chart simplifies the definition based on the criteria set forth by the International Ascites Club [21, 22].
The key factor in diagnosing HRS is the absence of improvement of kidney function despite discontinuation of potential nephrotoxic agents, and a trial of fluid repletion. Essentially HRS appears as a non-volume responsive pre-renal injury. This is why it is essential to rule out all other possible AKI systematically (Table 1).
AKI stage 1 is defined as the increase in serum creatinine (sCr) of >0.3 mg/dl within 48 hours or a > 50% percentage increase in sCr from a known or presumed baseline in the past 3 months which occurred within the past 7 days or urine volume < 0.5 cc/kg for 6 hours.
Changes in the definition of AKI in patients with cirrhosis has changed over time and has been replaced by the ICA (International Club of Ascites) AKI criteria [4, 23]. One of the most important changes was the removal of cutoff values of sCr for diagnosis of HRS in the setting of AKI, allowing earlier recognition and treatment of HRS.
Major diagnostic criteria include cirrhosis with ascites, presence of renal failure which helps differentiate HRS type I and HRS type II.
HRS type 1, renal failure is acute based on the KDIGO guidelines, increase in serum creatinine by ≥0.3 mg/dL within 48 hours; Increase in serum creatinine to ≥1.5 times baseline (i.e. 50% above baseline), which is known or presumed to have occurred within the prior 7 days; or urine volume < 0.5 mL/kg/h over a 6-hour period [23].
Type 2 HRS renal failure decline in renal function progresses more slowly, usually Cr >1.5. Diagnosis of HRS-type 2 be made either in the context of chronic kidney disease (CKD), that is, in a patient with cirrhosis and a GFR <60 ml/min per 1.73 m2 for >3 months (HRS-CKD) in whom other causes have been excluded, or in the context of AKI, defined as a renal dysfunction that does not meet criteria for AKI and lasts for less than 90 days.
KDIGO guidelines define CKD as abnormalities in kidney structure or function (GFR <60 ml/min/1.72 m2) that persist for more than 90 days, and acute kidney disease (AKD), as AKI or as abnormalities in kidney structure or function that persist for more than 90 days [9, 23].
A recent proposal in the European association for the study of the liver guidelines suggested that HRS-2 should be referred to as HRS-NAKI (hepato-renal syndrome non-acute kidney injury) [24]. This is due to many reasons. HRS 2 is poorly defined and is more of an assumption that chronic abnormalities in serum creatinine without a definite timeline, thus arriving at a new definition of HRS-2 is more challenging than expected.
It is proposed that the diagnosis of HRS-NAKI be made either in the context of CKD, that is in a patient with cirrhosis and a decrease in GFR greater than 3 months (HRS-CKD) or in the context of AKD, defined as a renal dysfunction that does not meet criteria for AKI and lasts for less than 90 days underlying factors such as diabetes, arterial hypertension causing nonalcoholic steatohepatitis which eventually lead to cirrhosis can simultaneously affect the kidneys causing CKD as well [23].
The new nomenclature may enable clinicians to define the presence of HRS-AKI superimposed on CKD in a patient with structural damage of the kidney, as evidenced by previous abnormal biopsy, renal ultrasonography or by significant proteinuria.
In the context of the new definition of HRS-AKI on CKD: HRS-AKI, there would be no evidence of chronic structural damage. For HRS-AKI on CKD in which there would be evidence of chronic structural damage such as chronic proteinuria and/or abnormal renal ultrasonography but with a high suspicion of HRS-AKI.
Other diagnostic criteria for hepatorenal syndrome include:
Failure of response to 48-hour volume expansion with albumin and discontinuation of diuretics.
Absence of current use of nephrotoxic medications.
Absence of macroscopic indication of structural kidney injury such as of proteinuria less than 500 mg per day, microhematuria (less than 50 red blood cells per high powered field) and normal kidney ultrasound [9, 21, 23] (Table 2).
|
Although the definition of HRS appears straightforward, there are many clinical challenges to consider when making a diagnosis. For instance, the usefulness of creatinine measurement in patients with cirrhosis may be limited for many reasons such as assay interference with bilirubin, reduced creatinine production in liver failure patients, muscle wasting and malnutrition [25].
Also using the urine output in patients with cirrhosis is limited as it can affected by other factors, for example decreased urine can be a normal in hypovolemic patients as they retain sodium or it can be simply increased secondary to the use of diuretics, [26, 27] despite that urine output remain a factor to look for, as was demonstrated by, Amathieu et al. who showed that reduction in urine output is associated with worse prognosis and 3-fold increased in hospital mortality [28].
These are just a few examples of how clinicians must use sound judgment when attempting to make a diagnosis of HRS. As mentioned earlier, it is important to stratify causes as it would impact both management and possibly the urgency for transplantation.
Differentiating ATN and HRS can also pose a challenge to clinicians. Pre-renal azotemia represents the leading cause of AKI in patients with cirrhosis, good history and physical examination of patients warranted to exclude causes of hypovolemia as discussed above.
Urine studies have been also sought to be helpful, with structural etiologies such as ATN, tubular injury limits sodium reabsorption and fraction excretion of sodium (FENa) is increased, typically by greater than 2–3%, using these cutoffs has been challenging owing to the fact that all patients with advanced cirrhosis have chronic renal hypoperfusion and have an FENa less than 1%, even in the absence of AKI [29]. Other studies such as urinary sodium (less than 40 milliequivalents per liter), low urine osmolality are suggestive of ATN although their use in HRS has been limited.
The fraction excretion of urea (FEUrea) is superior to FeNA in differentiating AKI-HRS from ATN, obtaining such tests is very important in HRS as most patients with HRS are on diuretics. Urinary sodium is known to be affected by use of diuretic which can falsely elevate the urine sodium. That is one main reason why FeNa has been excluded from HRS definitions.
Novel urine biomarkers of tubular injury have long been sought to differentiate AKI-HRS and ATN in patients with cirrhosis [30].
There are many biomarkers released by tubular injury. Among these, NGAL has been the most widely studied biomarker in patients with cirrhosis and showed the greatest diagnostic accuracy in differentiating ATN from AKI-HRS [9]. Cut-off of 0.2% has been widely used in distinguishing HRS from ATN [9]. Urinary NGAL seems to be superior to plasma concentrations and performs better when measured after the two-day volume challenge recommended in the management of any AKI including HRS [31].
At the current time human studies rely on expert adjudication for differentiating ATN from AKI-HRS owing to the limited availability of renal biomarkers and restricted use of kidney biopsies in such a high risk population.
HRS is one of the many causes of AKI in individuals with both acute and chronic liver disease. After correctly making a diagnosis of HRS, clinicians must address the underlying etiology of HRS. Patients that develop usually have cirrhosis, alcoholic hepatitis, liver failure, or fulminant hepatic failure from any etiology. Management of HRS is usually supportive, with the definitive treatment being reversal of the underlying liver pathology. In several patients, this means liver transplantation.
First line treatment of supportive management for HRS is using vasoconstrictors in combination with albumin to combat splanchnic arterial vasodilation [32]. The goal of treatment is to improve hemodynamic dysfunction by combatting the decreased circulating volume and increasing mean arterial pressure. The most common vasoconstrictors used are vasopressin analogues (terlipressin), norepinephrine, and somatostatin analogues such as octreotide and midodrine.
The vasopressin analogue Terilpressin is noted to have a greater affinity for the vasopressin 1 receptors in the splanchnic bed, it has been found to improve kidney function in patients with HRS with a decreased incidence of ischemia as compared to vasopressin [33]. Studies have demonstrated that continuous administration of Terlipressin is better tolerated and associated with fewer adverse effects as compared to intermittent bolus administration [34]. Continuous infusion of terlipressin in an outpatient setting has also been reported to be an effective, safe option of HRS treatment as a bridge to transplant [35, 36]. Terlipressin is considered as the first treatment of choice of HRS in Europe. Despite this fact, it is not currently approved by the Food and Drug Administration for use in the United States and Canada as a clear benefit of treatment in HSR has not been established.
Terlipressin was proven to be more effective than placebo in treating HRS type 1 although terlipressin use was associated with more adverse events such abdominal pain, nausea, diarrhea and respiratory failure [37].
While Terilpressin is the traditional first choice for HRS, norepinephrine is another option clinician can use as vasoconstrictive therapy. One large meta-analysis looking at randomized control trials in HRS compared the efficacy of various constrictive therapies. Terlipressin did demonstrate the most effective pressor to reverse HRS, but had an increased risk of adverse events. Norepinephrine was nearly as efficacious as Terlipressin, and although it was not able to provide the survival benefit as Terlipressin did have a better safety profile [38, 39].
Albumin has a role in maintaining plasma oncotic pressure and detoxification. One of the few indications for albumin administration is HRS; with existing studies in the literature that report the efficacy of albumin in the treatment of HRS [40]. Although albumin has been proven to help in HRS, the optimal treatment dose has not yet been established in guidelines. One large meta-analysis study did demonstrate a benefit with albumin, but optimal treatment dose with albumin has yet to be established. The study did demonstrate that a cumulative dose predicts a successful response to therapy [41].
Current recommendation is to use both albumin with Terlipressin as it has been shown that it improves its beneficial effect when compared to using terlipressin alone or placebo [34, 42].
Transjugular intrahepatic portosystemic shunt (TIPS) is a treatment option for those patients who fail to respond to pharmacologic therapy. TIPS reduces portal pressures by placing a stent between the portal and hepatic vein. This decreases portal pressure and vascular resistance by reducing endothelin-1 [43, 44]. This procedure has shown to improve kidney function in patients with HRS with a reduction in serum blood urea nitrogen, serum creatinine, and urinary sodium excretion [45, 46]. Although the TIPS procedure does improve elements of HRS, it was shown that there is limited evidence of survival benefit in patients with HRS [47] in addition to risk of development of hepatic encephalopathy which remains the greatest concern for clinicians. This is due to the portosystemic bypass shunt which results in bypassing the livers detoxifying function.
Renal replacement therapy (RRT) is an option for patients with HRS who progress to kidney failure and is most commonly done in patients awaiting liver transplant, or those with an acute reversible event. The role of RRT remains unclear due to lack of survival benefits as similar short term and long-term survival rates have been demonstrated as compared with non RRT treated patients [48].
HRS is an important entity in liver transplantation. Firstly, many patients waiting for liver transplant will develop HRS. This is owing to the fact that the indication for liver transplant is often advanced cirrhosis or decompensated cirrhosis with ascites. These conditions may also predispose for HRS. The 1-year probability of developing HRS in the presence of ascites is 20%, and the 5-year probability is 40%. The patient population at highest risk of complications are those with fluid retention, which is seen in advanced and decompensated cirrhosis [49, 50].
Secondly, in patients who have HRS the therapies mentioned above such as vasoconstrictors are used often as a bridge to transplantation. Therapies discussed above including vasoconstrictors may help, but the definitive treatment in HRS patients is often a transplant. Aggressive supportive care is unable to improve the recovery of kidney function in less than 50% of patients with HRS [50].
The concept of addressing HRS with a Simultaneous Liver and Kidney Transplant (SLKT) would seem to address both organ dysfunctions. However, HRS has the potential to be reversed by liver transplantation alone, and thus SLKT is not routinely considered in HRS. As mentioned in earlier sections, HRS is associated with many renal pathologies and it is possible for patients with HRS to develop end-stage renal disease after liver transplant alone. Long wait times for liver transplantation has led to a rise in the incidence of pre-transplantation renal dysfunction. The prolonged HRS and long-term RRT can lead to permanent renal damage. The permanent renal injury may lead to a decline in renal function that may not be adequate after liver transplant alone [42, 50].
HRS is not an uncommon entity in cirrhotic patients. It remains a challenge both diagnostically and in terms of management. Although there are many causes of renal impairment in the setting of cirrhosis, HRS is unique as the kidneys do not have an organic injury; rather they are a victim of poor circulation seen in advanced liver disease. Any renal impairment has the potential to increase mortality in the cirrhosis population, but HRS in particular is endangering to patients. There are two common forms of HRS, type 1 and type 2, and they can be generally distinguished based on acuity. There appears to be promise in the ease of diagnosis, with the advent of possible biomarkers; however, the present diagnosis is one of exclusion and can often be of challenge for clinicians. The management is mostly supportive care, with albumin and pressor playing a prominent role. The definitive treatment is addressing the underlying liver pathology, which often means liver transplantation. In some instances, there may be a simultaneous transplantation of the kidney and liver.
HRS | hepatorenal syndrome |
GFR | glomerular filtration rate |
AKI | acute kidney injury |
ATN | acute tubular necrosis |
CKD | chronic kidney disease |
HRS-NAKI | hepato-renal syndrome non-acute kidney injury |
AKD | acute kidney disease |
FENa | fraction excretion of sodium |
FEUrea | The fraction excretion of urea |
RRT | Renal replacement therapy |
SLKT | Simultaneous Liver and Kidney Transplant |
The livelihoods of Indians are mainly from agriculture. It has been noted in the last decade that there has not been much agricultural crop development. As crop prices decrease, food prices are constantly increasing. Since 2010 more than 40 million people have been driven into poverty [1]. This may be due to water wastes, low soil fertility, abuse of fertiliser, climate change and diseases, etc. There are numerous factors responsible Effective farm intervention is very important and IOT is the solution for integration with wireless sensor networks. It is capable of changing the way agriculture develops and contributes greatly to making smart agriculture. There is a three-tier system in the internet. It contains the layer, network layer and application layer of perception. Sensor motes include perception layer. Devices enabled by ICT, sensor motes are the building blocks of sensor technology. It comprises cameras, RFID tags, sensors and network sensors for object recognition and real-time information collection. The network layer is a universal service IOT infrastructure. The combination of the layer of perception and the application layer is directed. The layer of application is a layer that combines the IOT with specific industry technology. The internet has almost been applicable in all industries, including intelligent agriculture, smart parking, environmental monitoring for intelligent buildings, health transport and much more.
The Internet of Things (IOT) is the easiest and most powerful way to solving problems. IOT is established from different assemblies, with tonners of recorders, software, pieces of axes. It also makes details more detailed. Without human interference IOT permits the sharing of data over a network. We will mirror things naturally on the internet as everyday people like a sensor, a car driver etc. An IP address is given so that data can be transmitted across a network. In 2016, the number of connected devices grew 30% relative to 2015 according to the report produced by Garner. He adds that this number is set to grow by 26 billion by 2020 [2].
For the following factors, IOT technology is more efficient:
Faster Access
Efficient Communication
Time Efficiency
Global Connectivity through any devices
Minimum human efforts
In 2016, Patil and Kale [3] reviewed climate change and rainfall over the past decade as annoying. As a result, many Indian farmers are implementing climate-intelligent practices in recent times called intelligent agriculture. Smart farming is an integrated and IOT-driven information technology (Internet of Things). In all wireless settings IOT evolves easily and broadly. Within this article, the introduction of sensor systems and wireless communications to the implementation of IOT technologies and the systems in current agriculture are analyzed and evaluated, in conjunction with the current conditions of the agricultural system Remote Management System (RMS) is proposed to incorporate an approach with internet and cellular communications. Main aim is to capture real-time farming production data offering easy access to farming equipment such as fast massage service warnings and guidance on weather conditions, crops etc (Figure 1).
SAIoT model.
Mercedes, S., Bo, G. And Yuxia, H., in 2011 [4] investigated the vast species and huge quantity of insect parasites of field crops. Hundreds of common insect pests in farmland are caught by lamps. After the Black Light was trapped, the insect pests were manually recognized and numbered. And the process of predicting was used primarily for a long time in China. It was closely linked to the overall quality of the forecast person’s impact, accuracy and efficacy, and was ultimately determined by greater subjective factors.
Students studied the picture identification on field plants of insect pests in detail. The static digital camera pictures were categorized of 40 species (25 families, 8 orders) of nightmarish insect pests [5]. With gray-scale images, the 30-five typical plagues (Lepidoptera) manually put were recognized [6]. The auto trapped, killed, and placed into laboratory for further study the eight species of insect pests in cotton fields [7].
With the hardware and software framework the second-generation insect pest detection device was developed. Hardware provided trapping, astonishment and buffering, a uniform lighting, a dispersion-transporter and a vision machine. The software framework has improved image, segmented images, chosen functionality and known insect pests. The unit carried out the entire automation from the collection of insect pests to the identification.
See Figure 2.
Hardware components for real time monitoring system for insect pests on field crops. (1) “Trapping, stunning and buffering unit, (2) CCD (3) Illumination unit and (4) Scattering and transporting unit”.
Owing to the vast number of details and high efficiency in real time, the cost of the image processing equipment has been raised. Windows 2003 used the framework and the Visual C++ 6.0 language for visual development. MFC-based application software was developed using an OK C30S acquisition graphics card with API functions.
Real-time detection device for field plagues of second generation has been developed. It performed all automation, from the capture, dispersion, transport, collecting of images, picture analysis to pest recognition. It reduced the duration of detecting pests and increased the degree of automation. The right identification ratio of nine species of pests surpassed 86%. The study further focused on enhancing pest species and improving the detection efficiency.
Shi, Y., Z., Wang, X. Shi, Y. And Zhang, S. in 2015 [8] studied the efficient way of enhancing agricultural low-tech culture quality by using information and communication technology to establish a plant disease and long-term insect pest control system as the farm expert was unable to use the farm for farm management and insecticide disease control. The article introduces internet-based information perception technology (IOT) as well as the role of IOT technology for agricultural disorders and for the control of insect pests, including farming disabilities and the insect pest control system, the collection of sensor nodes, data processing and exploitation of insect pest information, etc. An IOT-based disease and insect pest control system consisting of three levels and three systems were proposed. A new way of accessing agricultural information on the farm is provided by the system.
“Sreekantha, D.K. and Kavya, A.M”, in 2017 [9] investigated the reorganisation of the IOT for agriculture, helping farmers to deal with problems in the region with a broad variety of technology including accuracy and sustainable agriculture. IOT technology helps to gather information on conditions such as weather, precipitation, soil temperatures and fertility, field tracking online allows weed identification, water level, pest detection and interference of livestock, crops growth and farming. IOT moves farmers from everywhere and at any time to link to their farm. Farms are monitored using wireless sensor networks, and microcontrollers for the control and automation of agricultural processes. Wireless cameras have been used to view the environments remotely in the form of pictures and images. A smart telephone enables farmers, at every time and anywhere in the world to keep up to date with the current conditions of their agriculture. IOT technology can cut costs and increase conventional agriculture’s productivity.
The Internet of things helps agricultural crop tracking to increase crop production and thus the farmers’ income quickly and effectively. A wireless sensor network and sensors of all kinds are used for the collection of crop conditions and environmental change and are transmitted to farmers/devices via a network to cause corrective action. Farmers are in connection with the circumstances of the agriculture sector around the globe at any moment and wherever. Few connectivity drawbacks have to be addressed by encouraging the technologies to conserve resources and also by facilitating the user experience.
“Ramalingam, B., Mohan, R.E., Pookkuttath, S., Gómez, B.F., Sairam Borusu, C.S.C., Wee Teng, T. and Tamilselvam, Y.K in 2020” [10] researched that early insect identification and control (human physical conditions as an example houses, hospitals hotels, parks, camps, flooring, industries related to food etc) and agricultural farms were important for developed environments. These pest control steps are currently labor-dependent manual, repetitive, unpredictable and time-consuming activities. Latest advances in Internet of Things (IOT) and Artificial Intelligence (AI) and the can automate a range of maintenance operations, improving efficiency and safety dramatically. This document includes the implementation of Deep Learning (DL) and IOT monitoring system of insect traps in real-time as well as the detection of insects.
System for remote trap control and insect detection based on IOT and DL.
In this article, the IOT and deep learning system was proposed for the remote insect tracking and the automated process of insect detection. The Faster version of RCNN ResNet object identification mechanism was accustomed to automately classifies the parasite type, using the Four-Layer IOT system to built the remote trap insect tracking mechanism. Included is an ecosystem insect data base and farm field insect archive that has been checked for offline and on-line reliability in the detection of intensely learning insects. According to other object recognition mechanisms such as SSD and Yolo, the accepted device provided optimal insect detection efficiency. The research has shown that 96 percent of insects identified with built-in environmental insects were obtained by the qualified model, 94 percent were identified with farmland insects, and 0.2 s were needed on average for processing the one image. This case study has demonstrated the automation of remote identification through IOT and a DL-based insect monitoring system using a qualified CNN framework and overcomes insect control systems failure.
“Potamitis, I., Eliopoulos, P. and Rigakis, I” in 2017 [11]. In many of our records, a large number of extreme insect plagues of agricultural and health importance were studied in a broad spatial scale as the principle of remote insect control. The trap is used to make the trap, the timeline, the GPS tag and where necessary, the inbound insect species from the wing beat safe to inject. Standard low-cost pest traps for certain insect species augment the insects. Both large crop insects are tracked in order to decide if a treatment strategy can be undertaken before a significant infestation takes place. Monitoring processes are based on specially designed mosquito traps. Conventional insect monitoring is used in the spectrum of such tracking. It takes physical labour, consumes money and also needs an expert to be accurate enough, often with the potential for raising human security concerns. It is limited on its own expenses.
These drawbacks decrease the amount of manual insect surveillance and thus its precision, which eventually contributes to considerable cultivation losses due to pest damage. You intend to monitor the existence, the stamping period, the detection of species and the population density of target plaguicides with ‘supervision’ to unmatched data extraction levels. Insect counts are wirelessly transmitted to the central supervisory agency, and predictive tools for the control of insect pests and environmental factors related to population growth are visualized and streamed through. The work illustrates how traps can be organized in networks that report collectively on local, state, world, continental and international data using the new Internet of Things technologies (IOT).
This research is undoubtedly cross-disciplinary, lies at the intersection of entomology, optoelectronic engineering and computer and crop-science and involves the production and introduction by many of the most important agricultural pests of low-cost low-energy technologies to minimize the amount of quantitative and qualitative crop failure. They claims that clever traps interacting through IOT will have a huge effect on the crops security decision-making process in real time and would undermine current manual practices directly from the field to a manually managed entity in the very near future. In this article three instances of Rhynchophorus ferrugineus are investigated: track the use of (a) picusan, (b) lindgren and (c) monitoring various stored grain beetle pests using sored grain pitfall trap for Rhynchophorus ferruginus (Olivier) (Coleoptera: curculionidae). There is a very detailed approach to the industry that delivers accuracy per cent on automated quantity when as opposed to the actual defined number of insects in each type of trap.
“Materne, N. and Inoue, M. in 2018” [12] examined the advancement in sensor technology has driven the technological revolution in agriculture. In these days, however the opportunity to use digital technologies relating to the Internet of Things (IOT) increase sharply; the growth of the roles system of farming continues in its early stages. The current threats of less preferred climate conditions thrive on the increased risk of cross-border plant pesticides and diseases that damage crops, as well as on the danger to the food security and on some major losses for the farmers, provided that agricultural sector is still suffering from climate changes. In this study, they merged Wireless Sensor Network (WSN) sensor devices to establish an agricultural field monitoring framework that simultaneously tracks eight primary environmental parameters that are known as highly interconnectioned to booming pests and plant diseases. The overall system configuration provided for real-time tracking and regular collection of the huge volume of data. This is why they have investigated the knowledge obtained using machine learning approaches using “KNN, Random Forest, Logistic Recovery and Linear Regression algorithms”. The purpose of this article is to perform an experiment on the advantage of using the IOT systems in agricultural lands to gather and analyze data to determine a prediction model that could be used to help forecast the outbreaks of plantation diseases.
They suggested an IOT framework with functionalities for day-to-day tracking of farmland environmental parameters. They also developed a predictive model for the provision by applying machine learning algorithms to avoid the outbreaks of pest and diseases in planting. The study merged IOT and machine learning technologies to improve agriculture and agriculture to draw on new concepts and developments in technology in order to sustain, increase yields and increase agricultural efficiency. The work represents only eight sensed parameters; the number of sensors, like meteorological information, may be added for potential production purposes. In addition, it is important to enhance the work of cloud providers so long as the behaviour and features of each type of pest and disease are taken into account.
“Deng, L., Wang, Y., Han, Z. and Yu, R” in 2018 [13] performed a study entitled “Research on insect pest image detection and recognition based on bio-inspired methods”. In this study, methods inspired by human visual systems were suggested to easily identify and identify insect pests. SUN was used for the generation of saliency and area of interest maps and identification in pest images using a Natural Statistics Model (NGM) to inspire human visual focus. In order to exclude invariant characteristics, the bio- influenced Hierarchic Model and X (HMAX) model were used to reflect a plague presence. Scale Invariant Function Transform (SIFT) in the HMAX model was integrated to improve rotational changes invariance. In the meanwhile, Coarse non-negative encoding (NNSC) simulates clear cell replies. Furthermore an Invariant Texture Properties has been removed using the Local Pattern (LCP) algorithm. Finally, the extracted characteristics were provided for recognition by Support Vector Machines (SVM). Experimental studies have shown a gain from the proposed approach relative to the approaches “HMAX, Sparse Coding and Normal Input Memory (NIMBLE), which is similar to the Deep Convolution Network” (Figure 4).
Sample images taken in natural conditions. (a) Locusta migratoria, (b) Parasa lepida, (c) Gypsy moth larva, (d) Empoasca flavescens, (e)
With an identification rate of 85.5%, the proposed approach obtained a successful outcome and could efficiently identify insect pests in diverse ecosystems. The suggested solution offered a new approach to the identification and recognition of insect pests.
“P. Tirelli, N.A. Borghese” [14] found that surveillance of the population of pesticides in the plant sector is currently a concern. At present, the device is based on dispersed images, which can be automatically collected and transmitted images of stuck areas to a remote station by means of a wireless sensor network. The station tests the density of the production of insects at various farm sites and alerts when the threshold of the insect is surpassed. The client nodes are spread in the fields that serve as monitoring stations. The main node co-ordinates the network and retrieves images from client nodes. During the four week monitoring cycle, the network periodically operates, and the viability is assessed, forecasts the population curve of the pest insects compared with everyday assessment.
“Nanni, L., Maguolo, G. and Pancino, F” in 2020 [15] performed a study entitled “Insect pest image detection and recognition based on bio-inspired methods”. Identification of insect pests is important for crop safety in many areas of the world. They introduce in this article an artificial classification based on the combination of saliency and neural networks. SALIENCY techniques are widespread image processing algorithms that identify the most important image pixels. In this paper you are using three different salience approaches as preprocessing of images and make three distinct images for each form of saliency. For each original image they generate new photographs to train various neural convolution networks, they create 3*3 = 9 new pictures (Figure 6).
Image samples from IP102.
They evaluate each execution of each preprocessing/network pair as well as they examine the performance of their grouping. You assess your solution on the major IP102 and a tiny dataset. Its best ensembles achieve the maximum degree of technical precision, with both the smaller (92.43%) and the IP102 (61.93%), matching the efficiency of smaller human experts (Table 1).
Species | Number of Samples |
---|---|
Locusta migratoria | 72 |
Parasa lepida | 59 |
Gypsy moth larva | 40 |
Empoasca flavescens | 41 |
Spodoptera exigua | 68 |
Chrysocus chinensis | 50 |
Laspeyresia pomonella larva | 50 |
Spodoptera exigua larva | 56 |
Atractomorpha sinensis | 62 |
Laspeyresia pomonella | 65 |
Composition of dataset.
Cardim et al. in 2020 [16] studied that certain insect pest species can be automatically identified and tracked. In order to develop integrated pesticide management (IPM) for precision agriculture, several systems were developed. For many essential pests, automatic detection traps were developed. This emerging methods and strategies are very promising to identify hostile and quarantine pests early on and to monitor them. The paper attempts to evaluate technological and scientific state-of-the-art sensor technologies in order to track and automatically identify insect pests (Figure 7).
Automatic trap for moth species tracking (a) and fruit flies (b) EFOS, Trapview, Slovenia.
“In the article the methods are discussed for the identification, the applications are introduced, as well as recent progress, comprising machine learning and the Internet of Things, infrared monitors for pests, audio sensors and classification through images”.
“Miranda, J.L., Gerardo, B.D. and Tanguilig III, B.T” in 2014 [17] performed a study entitled “Pest Detection and Extraction Using Image Processing Techniques”. Detecting pests in paddy fields is an important problem in agriculture, so effective steps to tackle infestation and reduce the use of pesticides should be created. Imaging techniques are commonly used in agriculture to offer optimum protection for crops, thereby leading to greater crop management and production. The surveillance of pests depends on workers but electronic control is developing to mitigate the efforts and mistakes of human beings (Figure 8).
Global architectural design.
This study broadens the application of various imaging procedures for the identification and extraction of insect pests through the establishment of an integrated paddy field detection and extraction device for estimating plague densities. Experimental review shows the proposed approach to detect pests in rice fields to provide a quick, reliable and simple solution.
“Xia, D., Chen, P., Wang, B., Zhang, J. and Xie, C” in 2018 [18], to solve the issue of multi-classification of insects in the field, evaluated a prototype of a neural network. The model will use the benefits of the neural network such that multifaceted insect traits are completely extracted. In the regional proposal process, rather than a standard, selection technique, the regional proposal network is implemented in order to produce fewer proposal windows, which are particularly valuable for improving the accuracy of forecasts and accelerating calculations. Experimental studies indicate that the proposed approach is better than the current conventional insect classification algorithms and is higher reliably (Figure 9).
The proposed detection model using VGG19 having schematic structure.
Thenmozhi, K. and Reddy, U.S., 2017 [19] performed a study entitiled “Image Processing Techniques for Insect Shape Detection in Field Crops”. In farming, the identification of crop pest is considered one of the farmers’ challenges. An integrated machine vision and picture analysis device for insect detection enables improved identification of early-stage plant insects with a lower time and greater precision that will improve crop yield. Digital image processing methods were used in the current work to detect the insect forms in the sugar cane crop with photographs of crop insects for pre-processing, segmentation and extraction. Sobel edge detecting is introduced to distal media. In the extraction of the feature nine geometrical features can be defined in the structure of the insect. This recognition of insect shape performs well and achieves high precision for round (circular), oval, triangular and rectangular sugarcane field insects. The study was carried out using the Image Processing Toolbox in MATLAB 2015b (Figure 10).
Flow chart for detecting crop insects form.
“Saha, A.K., Saha, J., Ray, R., Sircar, S., Dutta, S., Chattopadhyay, S.P. and Saha, H.N.”, in 2018 [20] researched that the need for increased population and agriculture is becoming increasingly frequent with unmanned air vehicles. Drones with suitable cameras, sensors and modules can help to make agriculture simpler, more effective and more accurate. The solutions proposed relating to these drones will help to expand the potential of further improvement if combined with various machine learning and the Internet of Things concepts. The relevant work in this field and the solutions which could be incorporated into the drone using the Raspberry Pi 3 B module were highlighted in this paper.
They conclude that drones or UAVs would be of tremendous help in agriculture as they are crucial at the start of a crop cycle with an increasing population. Not only will it minimize time, but it will also generate better cultivation based on analyzed data. The systematic monitoring will make crop management more effective. With the next developments, with less electricity usage, the output rate will increase rapidly. Drones are used in the planting of plant nutrients in the soil not just in soil and field analysis but also in planting seeds. The use of drones could also eliminate the crop monitoring obstacles that had previously been faced. Drones are not stopped here because they are integrated into hyper-spectral, thermal, or multispectral sensors and drones may detect which parts of the soil are dry. Furthermore, drones will also be used for scanning with near-infrared and visible light in order to determine crop health. Drones therefore act as an ideal aeroplane for the accuracy data collection.
“Zhong, Y., Gao, J., Lei, Q. and Zhou, Y” in 2018 [21] studied that a Design and implementation of flying insects counting and classification system based on vision. The system is built as follows: firstly, in the surveillance area a yellow sticky trap is installed to catch flying insects, and the camera is mounted to capture images on the spot. Then the process of detection and coarse counting on the basis of the detection and classification of objects by You Only Look Once (YOLO), using global functionalities is designed. Six insect species including fly, fruit fly, bee, mouth, moth, and chaff have been chosen to evaluate the efficacy of the system. The Raspberry PI insect counting and recognition system is implemented. The results of the tests show promising performance compared to conventional methods. On Raspberry PI the average counting accuracy is 92.5% and average classification accuracy is 90.18 percent. The system proposed is simple to use and provides fast and reliable identification data; it can therefore be applied for smart farming applications (Figure 11).
The components of agricultural monitoring service platform.
“Wang, Q.J., Zhang, S.Y., Dong, S.F., Zhang, G.C., Yang, J., Li, R. and Wang, H.Q.”, in 2020 [22] studied that Accurate agriculture poses new challenges for on-site pest monitoring in real time based on the new AI technology generation. This paper establishes a large-scale standardised data collection of agricultural pests, called Pest24 to provide a large data resource for the training of profound learning models for the detection of pests. In particular, the current data set consists of 25.378 images from our automatic plague trap and imaging system annotated with pests. 24 typical pest categories, which are mainly responsible for destroying field crops in China every year, are involved in Pest24. They use various cutting-edge methods of detection, such as RCNN Faster, SSD, YOLOv3 and Cascade R-CNN, to detect pests in the data set and to generate promising findings for in real time field pests monitoring. In the exploration of factors that impact on pest detection accuracy, the data set are analysed into a range of aspects and the three factors that mainly affect the performance of the pest detection, i.e. relative scale, number of instances and adhesion of objects. Overall, Pest24 usually features large-scale multi-pest image data, small object sizes, high object resemblance and dense pesticide distribution.
Numbers of images and instances of each pest category in Pest24 | |||
---|---|---|---|
Index | Pest name | Images | Instances |
1 | Rice planthopper | 316 | 1511 |
2 | Rice Leaf Roller | 944 | 1240 |
3 | Chilo suppressalis | 454 | 1285 |
5 | Armyworm | 3828 | 8880 |
6 | Bollworm | 9049 | 28,014 |
7 | Meadow borer | 5526 | 16,516 |
8 | Athetis lepigone | 7520 | 30,339 |
10 | Spodoptera litura | 1588 | 1951 |
11 | Spodoptera exigua | 3614 | 7263 |
12 | Stem borer | 1357 | 1804 |
13 | Little Gecko | 2503 | 4279 |
14 | Plutella xylostella | 531 | 953 |
15 | Spodoptera cabbage | 1707 | 2302 |
16 | Scotogramma trifolii Rottemberg | 3223 | 4679 |
24 | Yellow tiger | 1388 | 1686 |
25 | Land tiger | 369 | 475 |
28 | Eight-character tiger | 154 | 168 |
29 | Holotrichia oblita | 90 | 108 |
31 | Holotrichia parallela | 3111 | 11,675 |
32 | Anomala corpulenta | 5228 | 53,347 |
34 | Gryllotalpa orientalis | 3629 | 6528 |
35 | Nematode trench | 118 | 167 |
36 | Agriotes fuscicollis Miwa | 1814 | 6484 |
37 | Melahotus | 239 | 768 |
“Ray, P.P” in 2017 [23] studied that innovative science in agriculture has taken a new path with the introduction of the Internet of Things (IoT). IoT must be widely experimented to be applied in different agricultural applications at the emerging stage. In this paper, they review many possible IoT applications and the particular questions and challenges relating to improved agricultural IoT deployment. The devices and wireless communication technologies linked to IoT are evaluated extensively to meet the unique requirements of agricultural and agriculture applications. Research has been carried out on those IoT sensor systems which deliver intelligent and intelligent services to smart farming. Various case studies are provided to analyse the IoT-based solutions implemented according to their implementation parameters by different organisations and individuals and categories. The related issues in these solutions are also illustrated, while defining progress factors and potential IoT work maps.
“Jiang, P., Chen, Y., Liu, B., He, D. and Liang, C.”, in 2019 [24] examined that The Alternarial leaf spot, the brown spot, the mosaic, the grey spot and Rust are five common types of apple leaf conditions. However, the detection of apple diseases in current research is not reliable and easy to ensure that the apple industry is growing healthy. This paper provides a deeper learning approach aimed at the detection in real time of enhanced neural networks (CNNs). This article is the first use of the Apple Leaf Disease Data Set (ALDD) data augmentation and annotation technology which consists of laboratory pictures and complex images in real life. Based on this proposal a new apple leaf detection model that uses deep-CNN is proposed by integrating the GoogLeNet Inception and Rainbow concatenation system. Finally, five common Aple Leaf diseases using a dataset of 26,377 image of diseased Apple leaves are trained by the proposed INAR-SSD (SSD with Initiation and Reinbow Concatenation Module) model. The experimental results show that a high rate of 23.13 FPS 78.80 per cent of mAP is used for the INAR-SSD model in the ALDD. The results show that an early diagnosis approach to apple leaf disease has been established by the new INAR-SSD model, which detects these diseases in real time with greater precision and rapidity than before.
“Behmann, J., Mahlein, A.K., Rumpf, T., Römer, C. and Plümer, L.”, 2015 [25] studied that the early and accurate biotic stress detection is needed for effective crop protection. Important results have been obtained in recent years on the early identification of weeds, herbal diseases and insect pesticides in crops. The findings relate both to the development of optical sensors that are non-invasive high resolution and methods for data processing that can manage signals from the resolution, size and complexity of these sensors. Several machinery learning methods, such as vector machines and neural classification (supervised learning) networks, have been used for precision agriculture (unsupervised learning). These methods can measure both linear and non-linear models and require few statistics. Early detection of plant diseases by the use of supervised or unattended learning methods, and weed detection by the use of formal descriptors, is effective applications. A brief introduction to machine learning that an analysis of its potential to protect precision crops and an overview of instructive examples from several areas.
“Rupanagudi, S.R., Ranjani, B.S., Nagaraj, P., Bhat, V.G. and Thippeswamy, G.”, 2015 [26], examined that farmers suffer tremendous losses every year as a result of plague infestations and that affects their livelihoods. In this article we will address a new solution to this problem by continuous video processing, cloud computing and robotics monitoring of crops. The paper describes the methods for pesticide detection in one of the most popular tomato fruits in the world. “An insight into how in this project even the notion of the Internet of Things can be conceptualized”.
“Nisha, G. and Megala, J.” in 2014 [27] studied that wireless Sensor Automatic irrigation system based on a network to maximise agricultural water use. A network of wireless sensors and temperature sensors distributed with solid moisture in the field is the unit. The Zigbee Protocol is used for the management of sensor information and for the control by means of an algorithm with sensor threshold values to a microcontroller irrigation system. A solar panel and a mobile Internet guide are used for the unit. A wireless camera in the field with image processing techniques monitors the area of the disease. The device is low costs and benefits power independence in water-limited geographically isolated areas.
This study is performed by “Liu, H., Lee, S.H. and Chahl, J.S.” in 2017 [28]. In order to detect pesticides more effectively, researchers have developed different technologies. The existing sensing technology, however, is still limited to effective field applications. This review paper is designed for exploring relative technologies and finding a method for the sensing and detection of crop invertebrates such as butterflies, sauté, snails and slugs. Two main areas for the identification and detection of invertebrates were identified: acoustic sensing and vision system (MVS). The acoustic sensor is suitable for detection and identification of soil pests, stored grains and wood, whereas acoustic sensors must usually be fitted to inspection samples causing difficulties for efficient inland applications. MVS has the potential to detect and identify invertebrates in crops in a more efficient and flexible manner. The invertebrate identification technologies have recently been studied in detail with MVS, but the detection of infertile fields is relatively weak. This study summarizes current research deficiencies and discusses possible research directions.
This study performed by “Kim, S., Lee, M. and Shin, C.”, 2018 [29]. In this study cloud-based technology was built to manage information gathering, analysis and prediction in a shared framework. The proposed integrated system Farm as a Service (FaaS) assists high-level applications support through farms activity and observing and associated equipment, data and models management. Such a system records, connects and manages IoT devices and analyses information about the environment and growth. Furthermore, this study consisted of the IoT-Hub network model. Effective data transmission for every IoT device and communication for non-standard items is supported by this model and is highly reliable in communication in most difficult circumstances. IoT-Hub thus make sure the steadiness of agricultural environment-specific technology. Specific systems are implemented at different levels in an integrated agricultural specialist FaaS system. A strawberry infection prediction system has been designed and analysed, and this system has been compared with other infection models (Figure 12).
The Internet of Things (IoT)-Hub is used in this network model. FaaS: Farm as a Service; api: application programming interface; LoRa: Long Range.
“Liu, H., Lee, S.H. and Chahl, J.S.”, in 2017 [30] examined that the benefits of multi-spectral and hyperspectral vision systems have been demonstrated to detect such invertebrate pests efficiently and accurately. However, the identification of certain camouflaged pests on host plants has been restricted by only use of spectral details. Three-dimensional (3-D) representations are widely studied for multifaceted object recognition and scene perception in many fields. However, because of a lack of appropriate data collection methods and efficient algorithms, 3-D technologies have no invertebrate detection applications. “They created a multi-spectral vision system that enables the development of denser plant and pest clouds with multi-spectral images of UV, blue, green, red, and near-infraround”. An algorithm was designed to differentiate wide leafs from relatively large pests in nuclei at the noisy stage. The vision can be used as an automated pesticide sprayer sensor, or to support advanced pesticide monitoring systems.
Yan-e, D., in 2011 [31] studied that Agricultural IT (AIT) has been the most productive means and instruments for improving agriculture productivity and making maximum use of agricultural resources and has been widely used on all aspects of agricultural. The use of information technology from agricultural production as an important sub technology of AIT measures the level of agricultural computerization and the efficacy of decision-making on farm production. In this paper the methods for MIS intelligent agriculture design and architecture is discussed based on the implementation of the concept of management of agricultural knowledge and analysis of agricultural data characteristics (Figure 13).
Investigation in Crop Production of Agriculture Information Management Flow.
See Figure 14.
IoT architecture for
The above flow is a combination of IoT networks and machine learning models to predict and recommend the
All transmitted frames from the field to the base station formed in the actual format and load in the ML models. ML models in the network base station are already trained models for various crops disease properties and labels. These are able to classify the captured properties and recommend the disease and various possible treatments. After classification, if the model predicts infection then it suggests the medicines and track for future updates.
S. No | Author name | Paper description | Publication year |
---|---|---|---|
1 | Patil and Kale | In all wireless settings IOT evolves easily and broadly. Within this article, the introduction of sensor systems and wireless communications to the implementation of IOT technologies and the systems in current agriculture are analyzed and evaluated, in conjunction with the current conditions of the agricultural system Remote Management System (RMS) is proposed to incorporate an approach with internet and cellular communications. | 2016 |
2 | Mercedes, S., Bo, G. And Yuxia, H | Real-time detection device for field plagues of second generation has been developed. It performed all automation, from the capture, dispersion, transport, collecting of images, picture analysis to pest recognition. It reduced the duration of detecting pests and increased the degree of automation. | 2011 |
3 | Shi, Y., Z., Wang, X. Shi, Y. And Zhang, S | An IOT-based disease and insect pest control system consisting of three levels and three systems were proposed. A new way of accessing agricultural information on the farm is provided by the system. | 2015 |
4 | Sreekantha, D.K. and Kavya, A.M | A smart telephone enables farmers, at every time and anywhere in the world to keep up to date with the current conditions of their agriculture. IOT technology can cut costs and increase conventional agriculture’s productivity. | 2017 |
5 | Ramalingam, B., Mohan, R.E., Pookkuttath, S., Gómez, B.F., Sairam Borusu, C.S.C., Wee Teng, T. and Tamilselvam, Y.K | Latest advances in Internet of Things (IOT) and Artificial Intelligence (AI) can automate a range of maintenance operations, improving efficiency and safety dramatically. This document includes the implementation of Deep Learning (DL) and IOT prototype for the tracking of insect traps in real-time and the identification of insects. | 2020 |
6 | Potamitis, I., Eliopoulos, P. and Rigakis, I | Conventional insect monitoring is used in the spectrum of such tracking. It takes physical labour, consumes money and also needs an expert to be accurate enough, often with the potential for raising human security concerns. It is limited on its own expenses. | 2017 |
7 | Materne, N. and Inoue, M. | IOT framework with functionalities for day-to-day tracking of farmland environmental parameters. They also developed a predictive model for the provision by applying machine learning algorithms to avoid the outbreaks of pest and diseases in planting. | 2018 |
8 | Deng, L., Wang, Y., Han, Z. and Yu, R | To exclude invariant characteristics, the bio- influenced Hierarchic Model and X (HMAX) model were used to reflect a plague presence. Scale Invariant Function Transform (SIFT) in the HMAX model was integrated to improve rotational changes invariance. In the meanwhile, Coarse non-negative encoding (NNSC) simulates clear cell replies. Furthermore an Invariant Texture Properties has been removed using the Local Pattern (LCP) algorithm. | 2018 |
9 | P. Tirelli, N.A. Borghese | The device is based on dispersed images, which can be automatically collected and transmitted images of stuck areas to a remote station by means of a wireless sensor network. The station tests the density of the production of insects at various farm sites and alerts when the threshold of the insect is surpassed. | 2011 |
10 | Nanni, L., Maguolo, G. and Pancino, F | SALIENCY techniques are widespread image processing algorithms that identify the most important image pixels. In this paper you are using three different salience approaches as an image preprocessing and create three different images for each form of saliency. | 2020 |
11 | Cardim et al | For many essential pests, automatic detection traps were developed. This emerging methods and strategies are very promising to identify hostile and quarantine pests early on and to monitor them. | 2020 |
12 | Miranda, J.L., Gerardo, B.D. and Tanguilig III, B.T | Imaging techniques are commonly used in agriculture to offer optimum protection for crops, thereby leading to greater crop management and production. The surveillance of pests depends on workers but electronic control is developing to mitigate the efforts and mistakes of human beings. | 2014 |
13 | Xia, D., Chen, P., Wang, B., Zhang, J. and Xie, C | Experimental studies indicate that the proposed approach is better than the current conventional insect classification algorithms and is higher reliably. | 2018 |
14 | Thenmozhi, K. and Reddy, U.S. | An integrated machine vision and picture analysis device for insect detection enables improved identification of early-stage plant insects with a lower time and greater precision that will improve crop yield. Digital image processing methods were used in the current work to detect the insect forms in the sugar cane crop with photographs of crop insects for pre-processing, segmentation and extraction. | 2017 |
15 | Saha, A.K., Saha, J., Ray, R., Sircar, S., Dutta, S., Chattopadhyay, S.P. and Saha, H.N. | Drones with suitable cameras, sensors and modules can help to make agriculture simpler, more effective and more accurate. The solutions proposed relating to these drones will help to expand the potential of further improvement if combined with various machine learning and the Internet of Things concepts. | 2018 |
16 | Zhong, Y., Gao, J., Lei, Q. and Zhou, Y | The detection and coarse counting method on the basis of the detection and classification of objects by You Only Look Once (YOLO), using global functionalities is designed. Six insect species including fly, fruit fly, bee, mouth, moth, and chaff have been chosen to evaluate the efficacy of the system. | 2018 |
17 | Wang, Q.J., Zhang, S.Y., Dong, S.F., Zhang, G.C., Yang, J., Li, R. and Wang, H.Q | 24 typical pest categories, which are mainly responsible for destroying field crops in China every year, are involved in Pest24. They use various cutting-edge methods of detection, such as RCNN Faster, SSD, YOLOv3 and Cascade R-CNN, to detect pests in the data set and to generate promising findings for in real time field pests monitoring. | 2020 |
18 | Ray, P.P | The devices and wireless communication technologies linked to IoT are evaluated extensively to meet the unique requirements of agricultural and agriculture applications. Research has been carried out on those IoT sensor systems which deliver intelligent and intelligent services to smart farming. Various case studies are provided to analyse the IoT-based solutions implemented according to their implementation parameters by different organisations and individuals and categories. | 2017 |
19 | Jiang, P., Chen, Y., Liu, B., He, D. and Liang, C. | This article is the first use of the Apple Leaf Disease Data Set (ALDD) data augmentation and annotation technology which consists of laboratory pictures and complex images in real life. Based on this proposal a new apple leaf detection model that uses deep-CNN is proposed by integrating the GoogLeNet Inception and Rainbow concatenation system. | 2019 |
20 | Behmann, J., Mahlein, A.K., Rumpf, T., Römer, C. and Plümer, L. | The findings relate both to the development of optical sensors that are non-invasive high resolution and methods for data processing that can manage signals from the resolution, size and complexity of these sensors. Several machinery learning methods, such as vector machines and neural classification (supervised learning) networks, have been used for precision agriculture (unsupervised learning). | 2015 |
21 | Rupanagudi, S.R., Ranjani, B.S., Nagaraj, P., Bhat, V.G. and Thippeswamy, G | The paper describes the methods for pesticide detection in one of the most popular tomato fruits in the world. “An insight into how in this project even the notion of the Internet of Things can be conceptualized”. | 2015 |
22 | Nisha, G. and Megala, J. | A network of wireless sensors and temperature sensors distributed with solid moisture in the field is the unit. The Zigbee Protocol is used for the management of sensor information and for the control by means of an algorithm with sensor threshold values to a microcontroller irrigation system. | 2014 |
23 | Liu, H., Lee, S.H. and Chahl, J.S. | This paper is designed for exploring relative technologies and finding a method for the sensing and detection of crop invertebrates such as butterflies, sauté, snails and slugs. Two main areas for the identification and detection of invertebrates were identified: acoustic sensing and vision system (MVS). | 2017 |
24 | Kim, S., Lee, M. and Shin, C. | Cloud-based technology was built to manage information gathering, analysis and prediction in a shared framework. The proposed integrated system Farm as a Service (FaaS) assists high-level applications assistance through farms activity and observing the associated equipment, data and models management. | 2018 |
25 | Liu, H., Lee, S.H. and Chahl, J.S. | “They created a multi-spectral vision system that enables the development of denser plant and pest clouds with multi-spectral images of UV, blue, green, red, and near-infraround”. An algorithm was designed to differentiate wide leafs from relatively large pests in nuclei at the noisy stage. | 2017 |
26 | Yan-e, D. | In this paper the methods for MIS intelligent agriculture design and architecture is discussed based on the implementation of the concept of management of agricultural knowledge and analysis of agricultural data characteristics. | 2011 |
A literature review on IoT devices to identify and track insects in crop fields is discussed in this article. Each solution has been seen to have its possibilities and limitations. The species of pests must be increased and the identification must be improved. Detailed knowledge on the real-time and historical context is required to ensure effective control and allocation of capital. The Internet of Things allowed the rapid and efficient tracking of agricultural crops so as to increase the crop production and hence the farmer’s revenues. In order to gather information on crop conditions and environmental changes, the wireless sensors network and sensors of various kinds were used.
This information is transmitted to the equipment network which initiates corrective action. For remote insect control and automatic insect identification, IoT and deep learning technologies are used. The Faster RCNN ResNet IoT Target Recognition Platform can be used to automatically identify the insect type with a four-layer IOT for remote trap insect monitoring.
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In addition, the radioecological impact associated to the waste management strategy followed has been evaluated.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"José Luis Guerrero‐Márquez, Fernando Mosqueda Peña, Juan\nMantero, Guillermo Manjón, Rafael García‐Tenorio and Juan Pedro\nBolívar",authors:[{id:"57724",title:"Dr.",name:"Guillermo",middleName:null,surname:"Manjon",slug:"guillermo-manjon",fullName:"Guillermo Manjon"},{id:"111020",title:"Prof.",name:"Juan Pedro",middleName:null,surname:"Bolivar",slug:"juan-pedro-bolivar",fullName:"Juan Pedro Bolivar"},{id:"111022",title:"Prof.",name:"Rafael",middleName:null,surname:"Gacia Tenorio",slug:"rafael-gacia-tenorio",fullName:"Rafael Gacia Tenorio"},{id:"193940",title:"Dr.",name:"Fernando",middleName:null,surname:"Mosqueda",slug:"fernando-mosqueda",fullName:"Fernando Mosqueda"},{id:"193941",title:"Dr.",name:"Juan",middleName:null,surname:"Mantero",slug:"juan-mantero",fullName:"Juan Mantero"},{id:"193942",title:"Ph.D. Student",name:"José Luis",middleName:null,surname:"Guerrero-Márquez",slug:"jose-luis-guerrero-marquez",fullName:"José Luis Guerrero-Márquez"}]},{id:"55434",doi:"10.5772/intechopen.68236",title:"Diluted Thermopressurized Phosphoric Acid: A Gentle Proton Donor for Polysaccharide Acid Depolymerization and (Bio)processing",slug:"diluted-thermopressurized-phosphoric-acid-a-gentle-proton-donor-for-polysaccharide-acid-depolymeriza",totalDownloads:1710,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Phosphorus is a very important element for several metabolic pathways in all living organisms as exemplified by DNA, RNA, glucose and fructose-P, and adenosine triphosphate (ATP). The whole metabolism of phosphate in any living organism involves the catalysis carried out by many enzymes, such as kinases, pyrophosphorylases, isomerases, and phosphatases. Symptoms of hypophosphatemia include neurological dysfunction and disruption of muscle and blood cells and could be caused by malnutrition, failure to absorb phosphate, and metabolic syndromes. Phosphoric acid is widely used as an acidifying agent in a variety of pharmaceutical formulations as an acidulant, flavor, and synergistic antioxidant and sequestering. At the laboratorial and industrial territories, due to safety precautions, phosphoric acid may be considered a valid acid alternative for stronger and risky acids such as sulfuric, hydrochloric, and nitric acids. Furthermore, phosphoric acid, among the mineral acids, is less corrosive for steel and all goods made therefrom. Taking into account all these favorable arguments, the applied research at our laboratory (LQBB) is focused, with success, in the utilization of much diluted and moderately thermopressurized phosphoric acid (o-PA) in the pretreatment of polysaccharides for many biotechnological, as oligosaccharides production, important prebiotics for the human gastrointestinal tract.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"José Domingos Fontana, Marcela Tiboni and Heidegrid Siebert\nKoop",authors:[{id:"192320",title:"Prof.",name:"José Domingos",middleName:null,surname:"Fontana",slug:"jose-domingos-fontana",fullName:"José Domingos Fontana"},{id:"195692",title:"Dr.",name:"Marcela",middleName:null,surname:"Tiboni",slug:"marcela-tiboni",fullName:"Marcela Tiboni"},{id:"195694",title:"Dr.",name:"Heidegrid",middleName:null,surname:"Siebert Koop",slug:"heidegrid-siebert-koop",fullName:"Heidegrid Siebert Koop"}]},{id:"56294",doi:"10.5772/intechopen.68658",title:"Recent Trends in Phosphatase-Mediated Bioremediation",slug:"recent-trends-in-phosphatase-mediated-bioremediation",totalDownloads:2117,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Industrial effluents from tanneries and electroplating industries from small‐ and large‐scale sector industrial plants contain substantial amount of toxic heavy metal, which pollutes rivers and lakes, land, air and sea leading to imbalance of ecosystem and certain health issues to humans, animals as well as plants. The worldwide environmental regulations stipulate the reduction of heavy metals in the effluents to permissible levels before discharging into water bodies. Enzyme‐mediated precipitation of heavy metals affords a novel eco‐friendly method for remediation of toxic heavy metals from various industrial effluents like tannery, electroplating and dye industries. This chapter has paid attention to bacterial alkaline phosphatase (BAP) from Escherichia coli C90 and calf‐intestinal alkaline phosphatase (CIAP), which catalyses phospho mono‐ and diesters and produces inorganic phosphate (Pi). The Pi thus generated precipitates the heavy metals as metal‐phosphate complexes. The kinetic behaviour of both the enzymes with para‐nitrophenyl phosphate, ascorbic acid 2‐phosphate and α‐naphthyl phosphate was investigated at various pH regimes from 8 to 11. The chapter also explains in detail the descriptive information on the capability of BAP‐ and CIAP‐mediated precipitation of heavy metals, which is desirable and convenient method for the toxic heavy metals such as chromium, cadmium, nickel and cobalt.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"Gouri Chaudhuri, Uma Selvaraj, Venu Babu and Richard W.\nThilagaraj",authors:[{id:"192857",title:"Dr.",name:"Richard",middleName:null,surname:"Thilagaraj",slug:"richard-thilagaraj",fullName:"Richard Thilagaraj"},{id:"195962",title:"Dr.",name:"P. Venu",middleName:null,surname:"Babu",slug:"p.-venu-babu",fullName:"P. Venu Babu"},{id:"195963",title:"Dr.",name:"Gouri",middleName:null,surname:"Chaudhuri",slug:"gouri-chaudhuri",fullName:"Gouri Chaudhuri"},{id:"195964",title:"Ms.",name:"Uma",middleName:null,surname:"Selvaraj",slug:"uma-selvaraj",fullName:"Uma Selvaraj"}]}],mostDownloadedChaptersLast30Days:[{id:"56162",title:"Phosphoric Acid Industry: Problems and Solutions",slug:"phosphoric-acid-industry-problems-and-solutions",totalDownloads:5178,totalCrossrefCites:2,totalDimensionsCites:9,abstract:"Phosphoric acid (PA) is an important industrial chemical used as an intermediate in the fertilizer industry, for metal surface treatment in the metallurgical industry and as an additive in the food industry. The PA industry is spread out worldwide in Europe, Asia and America, including countries that operate phosphate rock (PR) mines and produce PA, phosphatic fertilizers and phosphate-based products.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"Benjamín Valdez Salas, Michael Schorr Wiener and Juan Ricardo\nSalinas Martinez",authors:[{id:"16436",title:"Dr.",name:"Michael",middleName:null,surname:"Schorr",slug:"michael-schorr",fullName:"Michael Schorr"}]},{id:"55389",title:"Thermochemistry and Kinetics of the Reactions of Apatite Phosphates with Acid Solutions (II)",slug:"thermochemistry-and-kinetics-of-the-reactions-of-apatite-phosphates-with-acid-solutions-ii-",totalDownloads:1877,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"The principal material in the phosphate ores is composed of calcium fluorapatite Ca10(PO4)6F2, in which the various components have been partially substituted by magnesium, sodium, carbonate, and hydroxyl ions. These substitutions affect the stability of the material and its reactivity toward the acid attack. The present chapter reports the influence of carbonates and magnesium on these properties. Using different calorimeters, dissolution experiments of carbonated and noncarbonated Ca and Ca/Mg apatites were carried out in acid solutions leading to thermochemical quantities. The results show that substitution of carbonate for F ions in the channel (to get A-type carbonate F-apatites) results in increasing the stability of the edifice, while substitution of CO3 for PO4 in fluor- or hydroxyapatites (to get B-type apatites) leads to a decrease in stability. The latter phenomenon was also observed when substituting magnesium for calcium in F-apatites. The presence of the former in the apatite structure results in an increase of the speed of dissolution in acid solution that is enhanced when carbonate is also replacing phosphate groups. Dissolution mechanism of synthesized Ca/Mg F-apatites seems to be a one-step process, while dissolution of a Gafsa (TN) natural phosphate to get superphosphate fertilizer is more complex.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"Mohamed Jemal",authors:[{id:"15449",title:"Dr.",name:"Mohamed",middleName:null,surname:"Jemal",slug:"mohamed-jemal",fullName:"Mohamed Jemal"}]},{id:"55219",title:"Occupational, Public and Environmental Radiological Impact Caused by the Phosphoric Acid Industry: The Case of Huelva (Spain)",slug:"occupational-public-and-environmental-radiological-impact-caused-by-the-phosphoric-acid-industry-the",totalDownloads:1355,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"The production of phosphate fertilizers usually uses as raw material sedimentary phosphate rock, which contains enhanced concentrations from U‐series radionuclides about 10–100 times higher than unperturbed soils. This fact implies the need for evaluating the radiological implications of this activity. In our case, the study has been performed in a large fertilizer industrial complex located at Huelva town (SW of Spain), where sedimentary phosphate rock has been processed since 1965 to 2010, generating annually an average of about 2.5 million tons of a by‐product called phosphogypsum (PG), which has been stored in big stacks 1 km away from Huelva city, covering 1000 ha. The fluxes of the radionuclides of interest along the production process and the effective doses received by the workers have been determined. In addition, the radioecological impact associated to the waste management strategy followed has been evaluated.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"José Luis Guerrero‐Márquez, Fernando Mosqueda Peña, Juan\nMantero, Guillermo Manjón, Rafael García‐Tenorio and Juan Pedro\nBolívar",authors:[{id:"57724",title:"Dr.",name:"Guillermo",middleName:null,surname:"Manjon",slug:"guillermo-manjon",fullName:"Guillermo Manjon"},{id:"111020",title:"Prof.",name:"Juan Pedro",middleName:null,surname:"Bolivar",slug:"juan-pedro-bolivar",fullName:"Juan Pedro Bolivar"},{id:"111022",title:"Prof.",name:"Rafael",middleName:null,surname:"Gacia Tenorio",slug:"rafael-gacia-tenorio",fullName:"Rafael Gacia Tenorio"},{id:"193940",title:"Dr.",name:"Fernando",middleName:null,surname:"Mosqueda",slug:"fernando-mosqueda",fullName:"Fernando Mosqueda"},{id:"193941",title:"Dr.",name:"Juan",middleName:null,surname:"Mantero",slug:"juan-mantero",fullName:"Juan Mantero"},{id:"193942",title:"Ph.D. Student",name:"José Luis",middleName:null,surname:"Guerrero-Márquez",slug:"jose-luis-guerrero-marquez",fullName:"José Luis Guerrero-Márquez"}]},{id:"56294",title:"Recent Trends in Phosphatase-Mediated Bioremediation",slug:"recent-trends-in-phosphatase-mediated-bioremediation",totalDownloads:2117,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Industrial effluents from tanneries and electroplating industries from small‐ and large‐scale sector industrial plants contain substantial amount of toxic heavy metal, which pollutes rivers and lakes, land, air and sea leading to imbalance of ecosystem and certain health issues to humans, animals as well as plants. The worldwide environmental regulations stipulate the reduction of heavy metals in the effluents to permissible levels before discharging into water bodies. Enzyme‐mediated precipitation of heavy metals affords a novel eco‐friendly method for remediation of toxic heavy metals from various industrial effluents like tannery, electroplating and dye industries. This chapter has paid attention to bacterial alkaline phosphatase (BAP) from Escherichia coli C90 and calf‐intestinal alkaline phosphatase (CIAP), which catalyses phospho mono‐ and diesters and produces inorganic phosphate (Pi). The Pi thus generated precipitates the heavy metals as metal‐phosphate complexes. The kinetic behaviour of both the enzymes with para‐nitrophenyl phosphate, ascorbic acid 2‐phosphate and α‐naphthyl phosphate was investigated at various pH regimes from 8 to 11. The chapter also explains in detail the descriptive information on the capability of BAP‐ and CIAP‐mediated precipitation of heavy metals, which is desirable and convenient method for the toxic heavy metals such as chromium, cadmium, nickel and cobalt.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"Gouri Chaudhuri, Uma Selvaraj, Venu Babu and Richard W.\nThilagaraj",authors:[{id:"192857",title:"Dr.",name:"Richard",middleName:null,surname:"Thilagaraj",slug:"richard-thilagaraj",fullName:"Richard Thilagaraj"},{id:"195962",title:"Dr.",name:"P. Venu",middleName:null,surname:"Babu",slug:"p.-venu-babu",fullName:"P. Venu Babu"},{id:"195963",title:"Dr.",name:"Gouri",middleName:null,surname:"Chaudhuri",slug:"gouri-chaudhuri",fullName:"Gouri Chaudhuri"},{id:"195964",title:"Ms.",name:"Uma",middleName:null,surname:"Selvaraj",slug:"uma-selvaraj",fullName:"Uma Selvaraj"}]},{id:"55434",title:"Diluted Thermopressurized Phosphoric Acid: A Gentle Proton Donor for Polysaccharide Acid Depolymerization and (Bio)processing",slug:"diluted-thermopressurized-phosphoric-acid-a-gentle-proton-donor-for-polysaccharide-acid-depolymeriza",totalDownloads:1710,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Phosphorus is a very important element for several metabolic pathways in all living organisms as exemplified by DNA, RNA, glucose and fructose-P, and adenosine triphosphate (ATP). The whole metabolism of phosphate in any living organism involves the catalysis carried out by many enzymes, such as kinases, pyrophosphorylases, isomerases, and phosphatases. Symptoms of hypophosphatemia include neurological dysfunction and disruption of muscle and blood cells and could be caused by malnutrition, failure to absorb phosphate, and metabolic syndromes. Phosphoric acid is widely used as an acidifying agent in a variety of pharmaceutical formulations as an acidulant, flavor, and synergistic antioxidant and sequestering. At the laboratorial and industrial territories, due to safety precautions, phosphoric acid may be considered a valid acid alternative for stronger and risky acids such as sulfuric, hydrochloric, and nitric acids. Furthermore, phosphoric acid, among the mineral acids, is less corrosive for steel and all goods made therefrom. Taking into account all these favorable arguments, the applied research at our laboratory (LQBB) is focused, with success, in the utilization of much diluted and moderately thermopressurized phosphoric acid (o-PA) in the pretreatment of polysaccharides for many biotechnological, as oligosaccharides production, important prebiotics for the human gastrointestinal tract.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"José Domingos Fontana, Marcela Tiboni and Heidegrid Siebert\nKoop",authors:[{id:"192320",title:"Prof.",name:"José Domingos",middleName:null,surname:"Fontana",slug:"jose-domingos-fontana",fullName:"José Domingos Fontana"},{id:"195692",title:"Dr.",name:"Marcela",middleName:null,surname:"Tiboni",slug:"marcela-tiboni",fullName:"Marcela Tiboni"},{id:"195694",title:"Dr.",name:"Heidegrid",middleName:null,surname:"Siebert Koop",slug:"heidegrid-siebert-koop",fullName:"Heidegrid Siebert Koop"}]}],onlineFirstChaptersFilter:{topicId:"490",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. 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He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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