List of the VIs used in this chapter.
\r\n\tThere will be a chapter on secondary causes of sexual dysfunction disorders related to diabetes, cardiovascular disease, and obesity. A chapter on remedial measures to enhance sexual activity and maintain human relationships will be discussed. As there is a growing number of cancer survivors a chapter on cancer-related sexual dysfunction will be welcomed for including it.
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Therefore, monitoring food quality is an important and constant element of the food products market. This need arises from health issues, consumers’ religious convictions, and economic reasons. According to the WHO, in Europe, 8% of children and 4% of adults are allergic to bovine milk or hen eggs. While these products can be rapidly and easily identified in pure form, their presence in complex products may be much more difficult to detect. Knowledge of the species composition of these products, although unavailable without detailed analyses, is crucial for many patients. Likewise, religious convictions of many communities provide a powerful incentive for monitoring real composition of the food. For example, Judaism prohibits the consumption of pork, so a large part of the followers of this religion avoid the meat of pigs and replace it with beef or sheep meat, which form a considerable part of the meat market in these countries. Unfortunately, for economic reasons, food products are often intentionally adulterated by replacing declared, more expensive components with cheaper substitutes (e.g., meat of lower quality or plant fillers). There are also cases when the quantitative share of an expensive component in a complex product is lowered. By way of example, poultry meat is on average several times cheaper than pork, which, in turn, is priced lower than beef or lamb meat. Similarly, beef is cheaper and more readily available than game meat. The price differences may induce some unfair producers to adulterate and place on the market products whose components differ from manufacturer specifications.
The declaration of meat products in the EU is mandated by the Commission Directive 2002/86/EC [1] stating that meat products have to be labeled with precise information about the species and its percentage in the product. Nevertheless, as experience shows, there are numerous examples of components being misrepresented to make a product more attractive, justify a higher price, or enter new markets. Here, it suffices to mention that in products like fast food 65% of adulteration is deducted [2, 3] and in preparations of game meat, the percentage of factually inaccurate labeling is less (30%) [4], but in sausage, this percent has grown to 90% [5]. Both food products and pet foods were found to be adulterated, and Okuma found 40% of foods for animals with meat of chicken to be falsified [6]. Based on the information reported above and day-to-day practice, it could be claimed that food adulteration is becoming a global problem, which attracts consumer attention at international level and increases public concern about the quality of food products. By way of example, in 2013, the horse-meat scandal revealed gaps in the food safety system and undermined trust between producers and consumers.
It is, therefore, essential to identify the methods for (quantitative and qualitative) determination of species composition of food ingredients to monitor the conformity of a product with the description provided by the manufacturer. Research in this area can better protect consumers from illegal and undesirable adulteration, for whatever reason.
It should be also mentioned that recent years have seen increasing awareness of the importance of food safety and quality, which increases public interest in this issue and leads to changes in legislation. This necessitates continuous development and improvement of analytical methods.
The analysis most often uses mtDNA, although exceptions outlined below are permitted. The advantage of mitochondrial over genomic genome results from its resistance to the action of physical factors such as temperature and pressure, which very often accompany the processing of food. These characteristics of mtDNA contribute to a very high sensitivity of the analyses. In principle, the whole mitochondrial genome can be used for the analyses, but more frequent use is made of cytochrome B and D-loop. Cytochrome B is the most conservative of the entire mitochondrial genome. Its identification and creation of a bar code were the subject of projects aimed to describe all living organisms—both the most common and the most unique. In turn, D-loop is characterized by the highest variation between species, which enables the method to be quickly determined. The mitochondrial genome is very short compared to the body’s entire genome and forms a very small proportion of it. In animals, it is slightly over 16,000 bp, which means it is relatively easy to develop methods for identifying the panel of organisms chosen by a researcher. Current research papers present several methods from identify single farm species such as pigs [7, 8, 9], cattle [7, 8, 9, 10], sheep [7], horses [9, 11], chickens [9, 12], turkeys [9], ducks [8, 13], fish [14] to less common animals like kangaroos [15], snails [16], and marine animals like octopuses [17], shrimp [18] and sharks [19]. This is relatively the simplest method of analysis. With proper time investment, labor inputs, and funds, a laboratory is capable of identifying a concrete species. Such methods are generally very sensitive and enable determining adulterations as low as 0.001% [20, 21, 22], although this has little practical use because determinations below 1% are generally treated as artifacts. For this reason, the laboratories that commercially used methods most often set the limits of determination between 0.1 and 1% [23]. In certain cases, it is more beneficial to determine a whole group of animals rather than single species. These methods are more demanding because the reaction conditions have to be adjusted as to make the method specific for several DNA fragments that differ in sequences. The primers most commonly used are compatible with DNA of several species, which necessitates finding the most homologous fragments. Most often, however, the primers are homologous only in a certain percentage [19, 24]. Such analysis very often yields products of similar, indeed identical, length. Sometimes, it is, therefore, more beneficial to design one primer compatible with all species and another primer specific for single species, which gives products of different length [23, 25]. The choice of method depends on needs. Increasingly often laboratories face the challenge of discriminating between animal and plant DNA in a sample. This apparently easy task is in fact more complicated than identifying smaller groups of animals and impossible to perform based on mtDNA identification. Most often, animal DNA is identified using a DNA fragment that encodes myosin, a muscle protein; that is why myosin-based methods yield a positive reaction only for samples that contain muscles. This limitation may be a problem during analysis because the method allows no identification of matrices such as bones. Another limitation is the differentiation of animals with very similar mitochondrial genomes. This problem can be seen, for example, when distinguishing between pig (
All the identified DNA fragments should be short, less than 250 bp. There is the rule that the more the food product is processed, the shorter the PCR product should be.
Extreme temperature and pressure cut DNA into short segments; for example, exposure to a pressure of 3.2 Ba results in approximately 100-bp segments and only such or short DNA fragments can be identified. Of course, in raw or cooked meat, DNA is not degraded so much, but the method involving short DNA fragments is more universal and enables determinations to be made whatever the degree of processing.
Molecular methods enable determination to be made in any matrix. In practice, DNA can be identified regardless of matrix form or earlier processing. We can freely determine species composition of both raw tissues and processed tissues in the form of meat, bones, blood, eggs, dairy products such as cheese, milk and butter, drinks, gelatin, lyophilized milk products, meat preparations, and egg products [7, 12, 27, 28, 29].
It often happens that the matrices in which DNA is sought have a form that prevents its biological origin to be clearly identified, and so it may become a source of potential problems. This is exemplified by a fragment of biological material found by a consumer in meatballs [13]. The object concerned, which was small in size and additionally resembled a human nail (Figure 1), was identified during the analysis as material coming from one of the breeding species, so its presence in food preparations was fully justified.
Biological material found by a consumer in meatballs.
The most effective methods of species identification are based on PCR technique. These methods use both conventional PCR and real-time PCR. Both methods can be used as monoplex or multiplex PCR. Detection in real-time PCR can use both probes and DNA-binding dyes (e.g. SYBR Green, Eva Green). A detailed schematic representation of the method is given in Figure 2.
Detailed schematic diagram of the methods used for species identification.
Each method has its pros and cons. The simplest method, conventional monoplex PCR, is unbeatable when one concrete species is sought. These methods generally have a very high limit of determination, which is often so high that it has no practical application in commercial analyses. This figure, often below 0.001%, acquires real significance when determining undesirable trace substances or accidental artifacts.
Such methods are simplest but at the same time show the least potential, and only allow determining if a given substance contains the DNA of the species being identified.
Multiplex reactions not only offer more possibilities but also cause more problems. Since they require carrying out the reaction in one temperature, which is not necessarily optimal for all primers and as a result reactions may take place with different efficiencies, this may lead to false-negative reactions when the level of adulteration is low. Thus, although multiplex reaction unquestionably shortens the time of analysis and reduces its costs, when complex products are analyzed, the result for low content DNA can be subject to risk [30].
Another group of methods is restriction fragment length polymorphism (RFLP). This technique is based on amplification of a DNA fragment with different sequences, followed by its digestion with appropriate restriction enzymes, which enables even related species to be distinguished [31]. The method allows for identification of several to 25 animal species, although the latter requires the use of several restriction enzymes.
The PCR-RFLP method is simple, inexpensive, and easy to use for monitoring purposes. PCR-RFLP has been used for years and many researchers consider it outdated. However, this method works very well in the case of complex analyses, where we are interested in finding the potential presence, for example, of a group of species (e.g., birds, ruminants) and then their specific representatives. Similarly to the case of multiplex reaction, this method performs better for single-species samples, while their application for complex products may cause read errors, firstly because of similar restriction patterns for the analyzed species of animals, secondly due to the competitiveness of RFLP reaction. Another disadvantage of the PCR-RFLP method is that erroneous results may develop because of the occurrence of incomplete digestion of the restriction site or intraspecific differences, which may contribute to the removal or development of restriction sites [32].
When we analyze samples whose composition is completely unknown and has to be identified, Sanger sequencing is a very good solution. If we analyze a fragment homologous to several species, we can quickly and accurately determine its species origin. Again, this method is better applied to single-species samples and it is not a method of first choice for routine determination of specific species, if only because of higher price and the need to use more specialist equipment. However, it is an indispensable tool for analyses subject to greater uncertainty.
Another group of methods are quantitative determinations. They continue to be a major challenge for researchers because sample reactivity depends on processing method, type of matrix, and sometimes the animal. Therefore, production of the reference material that is later used to generate standard curves is subject to error of 10% or sometimes even 30%.
The production of reference material is an important issue when determining the type of meat. It should be noted that the certified reference material (CRM) is only available in the form of DNA, which in the case of quantitative tests does not work and is completely unsuitable because the mismatch of such material to the analyzed meat samples can be huge. That is why laboratories themselves produce reference materials. Usually, meat samples purchased commercially from the butcher or shop are used for this. It is important that they came from a few or several individuals. The material produced in this way is more precisely matched to the analyzed samples and has a lower risk that it will not completely match it. Before using the reference material so manufactured, it should be checked. First, the standard curve obtained from it must meet certain parameters such as slope, y-intercept, R2 value, and amplification efficiency (EFF%). The appropriate numerical values for these parameters guarantee the specificity and reaction efficiency of the standard curve used. The second necessary condition is the analysis against this curve of a sample with a guaranteed concentration of the species being determined. Such samples are most often obtained as residues from proficiency tests. It should also not be forgotten that the method of isolating DNA from reference material should be the same as the test samples [33]. Many authors use methods that match the largest number of food-related matrices, e.g. CTAB [33], although this depends on the experience and preferences of each laboratory.
Standard amounts of the material needed for the analyses range from 0.1 to 0.5 g because such amounts are most often recommended by the manufacturers of DNA isolation kits, but when determining microtraces in foods, we must often settle for a fraction of this weight. Since mtDNA is most commonly used, which allows for very sensitive analyses because it is present in every cell in many million copies, often trace amounts of material are sufficient to perform the analysis.
The high sensitivity of mtDNA-based PCR methods is a great advantage, but at the same time, this is associated with a serious risk of cross reactions. Therefore, the tests described above must be governed by a strict application of several rules, which, by design, should make the work more efficient and safe for the laboratory technician while ensuring the quality and effectiveness of the experiments.
The overriding rule is to perform most of the procedures in a laminar flow cabinet, in which air is constantly blown out to ensure sterile conditions. Prior to the commencement of work, it is a good practice to switch on the unit for more than 10 minutes, which will allow for a complete exchange of air, and to turn on the UV lamp, which is usually part of the unit, to make the work area sterile. The working area must be wiped with a DNA-removal solution. Before starting the job, make sure all necessary equipment and materials are ready at hand. At the same time, the working space must be divided into a “clean zone” (pipettes, centrifuges, vortex mixers, reagents, pipette tips) and a “dirty zone” (used tips and basket). These zones must be separated to avoid cases where a used pipette tip is carried over the reagents, test-tube stand, etc. Laboratory technicians working in a laminar flow cabinet should be adequately prepared for work. To ensure sterility, they should wear protective aprons and disposable gloves, additionally cleaned with a DNA-removal agent.
It is also important to separate workstations at which different stages of the analysis (sample preparation, DNA isolation, PCR, electrophoresis) are performed. Any change in workstation requires that the protective apron and disposable gloves be changed. One workplace must not overlap with another. Before starting and after completing the job, working surfaces must be cleaned with a DNA-removal agent. A laboratory sample should be moved in one direction only, in accordance with each successive stage of determinations. Test equipment must be regularly verified and calibrated.
An important aspect of work at a laboratory engaged in species DNA identification is validation of methods before they are introduced. An essential requirement for every research or scientific laboratory that performs commercial testing is to use reliable methods. The methods taken from ISO/IEC or recommended by umbrella organizations (e.g., EURL-AP) have already been validated, so it is enough to check their function in the laboratory. It should be noted, however, that in the DNA research area concerned, many laboratories use their own methods. These have the advantage of being flexible and adaptable to the current needs of customers, which means that the laboratory can react quickly and optimally to the evolving market needs. Naturally, these methods have to be validated, which incurs additional charges for the laboratory:
increased costs; before the method becomes profitable, the laboratory must usually pay high validation costs,
time-consuming nature; there must be adequate time between the decision to introduce a method to its real application in the laboratory. The longer and more laborious the validation process, the longer the time needed,
the need of training; it increases the costs and delays the practical implementation. However, this has a positive aspect for the laboratory in the form of better trained and more aware staff.
The high requirements placed on the personnel are naturally shaping the quality system, in which all employees are aware of their responsibilities, the work is safe, and ensures reliable results. Nowadays, most laboratories want to introduce a defined quality system. The most popular system is ISO/IEC 17025, which provides requirements for testing and calibration laboratories. Since its publication in 1999 by the International Organization for Standardization, the regulations in this document help to organize work in laboratories. Implementation of this standard certifies that all tests performed in the laboratory meet the standard and respect the chosen testing procedure. Because species identification is directly linked to food safety monitoring, introduction of the system provides measurable benefits in the form of growing prestige of the laboratory, increased efficiency, greater competencies of the managerial staff, clearly defined responsibilities and rights of the staff, increased testing accuracy, and higher number of commissioned tests.
The accreditation requirement most often results out of external pressure, from the customer or the regulatory authority [34], but sometimes it may result from the internal desire to increase the level of testing services [8] or even from institutional strategic planning [10]. However, decision to adopt ISO/IEC should consider (1) the organization’s culture, (2) the actual need for pursuing accreditation—the accreditation requirement from the customer or the regulatory authority, (3) the time and the resources available, (4) the staff’s knowledge and previous experience in quality, (5) the current conditions of the laboratory with reference to compliance with the standard, (6) use of standard test methods already established and known well by the laboratory staff, and (7) condition of equipment used for tests, in addition to involving appropriate costs of maintenance and calibration [34].
Modern methods based on mtDNA are a powerful tool for food analysis, creating great opportunities for the researcher, at the same time causing a number of challenges for the contemporary laboratory. The newly developed, commercially used methods are made taking into account the above-mentioned activities.
Bananas (
For treatment of the disease, and for crop planting adjustments, real-time monitoring and effective identification of banana Fusarium wilt play a significant role [5]. Traditionally, soil investigations have been the only effective means to monitor crop diseases, but such surveys take a lot of time and are often expensive. Recent years have witnessed the rapid development of the remote sensing technology, which has developed into a viable method for disease assessment and monitoring. The leaf pigment content, leaf area index (LAI) and water content of a plant which is infected with a disease will all undergo changes. And such biochemical and biophysical changes in the plant will also present in its spectral reflectance characteristics [6]. Remote sensing technology has been applied to monitor diseases, including Fusarium head blight [7, 8], rust infection [9, 10, 11], and powdery mildew [7, 8, 12, 13] in wheat, grey leaf spot in maize [14], bacterial leaf blight in rice [15, 16], and late blight disease and bacterial spot in tomatoes [17, 18] in some studies. However, the sensitivity of spectral bands and VIs varies with the category of diseases. For example, Bravo et al. [19] calculated the normalised difference vegetation index (NDVI) using wavelengths of 620–640 nm and 740–760 nm for extracting powdery mildew from wheat patches. Devadas et al. [20] distinguished yellow rusted wheat from healthy wheat using the anthocyanin reflection index (ARI). Huang et al. [10] suggested that the position of the red edge can be used as a disease indicator. With this in mind, it is of essence to identify which spectral bands and VIs are suitable for the identification of which specific diseases.
UAV remote sensing technology has been developed rapidly over recent years. It has become of interest due to its advantages of long flight time, real-time image transmission, effective detection of high-risk areas, low cost and easy manoeuvrability. It provides new means for the timely and non-destructive extraction of infected plants from the in-season crops [21]. Using UAV multispectral and hyperspectral images, a great number of studies have achieved significant progress in growth monitoring, crop classification, and the identification of diseases and insect pests [22, 23, 24]. Within banana production, a few studies have adopted UAV-based images to map the spatial patterns of photosynthetic activity in banana plantations [25]. Nonetheless, there are few studies that use UAV-based remote sensing to monitor banana Fusarium wilt [26, 27]. Furthermore, the spatial scale for remote sensing information and scaling remains one of the fundamental problems in geoscience [28]. Selecting an optimal spatial scale for remote sensing imagery plays a significant role in agricultural monitoring in particular.
Therefore, the goals of this chapter are to: (i) develop an identification method for Fusarium wilt based on UAV multispectral remote sensing, (ii) determine the optimal VI needed for the establishment of a quality identification model, and (iii) evaluate how different image resolutions affect the accuracy of Fusarium wilt identification in order to provide guidance for the application of satellite-based data in a massive scale.
The experiments were carried out at two experimental locations in Guangxi and Hainan, respectively.
The Guangxi experiment site is located in Guangxi Province of China (23°7′53″ to 23°8′4″ N, 107°43′45 to 107°44′7″ E) (Figure 1). It has a subtropical monsoon climate characterised by year-round sunshine and rainfall, with a mean annual temperature between 20.8 to 22.4°C, and an average annual rainfall of 1200 mm. The soil type according to the FAO soil classification system is Ferralsol [29]. The banana variety in the study area was “Williams B6”. The leaf number of this variety is 34–36, the plant height is about 2.4–3 m, and the growth period is 10–12 months. The banana plantation was established in September 2015, with the planting distance of 2.0 m by 2.6 m. The first harvest was carried out in November 2016. As of August 2018 (the time of the field investigation discussed in this chapter), the third generation of bananas was in the fields and more than 40% of the banana plants were infected with Fusarium wilt.
Location of the experimental sites with the survey sites.
The Hainan experiment site is located in Hainan Province, China (19°49′4″ to 19°49′16″ N, 109°54′40″ to 109°54′53″ E) (Figure 1). It has a tropical monsoon climate characterised by year-round sunshine and rainfall, with a mean annual temperature between 23.1 to 24.5°C and an average annual rainfall of 1750 mm. The soil type according to the FAO soil classification system is Humic Acrisol [29]. This experimental field was divided into two sub-fields (left area and right area) with the middle road as the boundary (Figure 1). The left area was developed in June 2017, with the planting distance of 2.0 m by 2.3 m. The first harvest was carried out in July 2018. The banana variety was “Baxijiao”. the plant height of this variety is about 2.6–3.2 m and the growth period is 9–12 months. In this field, the rate of banana Fusarium wilt infection was about 10%.
The right area was developed in August 2018. The planting distance was the same as that in the left field. The banana variety was “Nantianhuang”. The plant height of this variety is about 2.5–3.0 m and the growth period is 10–13 months. At the time of the field investigation in December 2018, no banana plants were found to be infected with Fusarium wilt.
In this chapter, the experimental data obtained from the Guangxi site was used for calibration and validation of the Fusarium wilt identification model, and from the Hainan site used for model validation.
The experiment at Guangxi site was carried out on August 7, 2018. A total of 120 sample plots were investigated to assess the occurrence or non-occurrence of Fusarium wilt (Figure 1). Among them, there were 57 healthy samples and 63 diseased samples. The size of each sample plot encompassed one banana plant. Eventually, 75% samples were randomly extracted and employed for the construction of Fusarium wilt identification model denoted by modelling dataset (MD); and the remaining 25% for model validation, denoted by validation dataset 1 (VD1). The experiment at Hainan site was performed on December 11, 2018. The survey strategy was in line with that of the experiment at Guangxi site. A total of 35 sample plots were finally investigated, of which 16 were healthy and 19 were diseased. All the sample plots from Hainan sties were served for model validation, denoted by validation dataset 2 (VD2).
The surveys were carried out by a DJI Phantom 4 Pro quadcopter (DJI Innovations, Shenzhen, China) equipped a MicaSense RedEdge-M multispectral camera (MicaSense, Inc., Seattle, WA, USA). The camera is configured with five bands: Blue (475 nm center, 20 nm bandwidth), Green (560 nm center, 20 nm bandwidth), Red (668 nm center, 10 nm bandwidth), Red edge (717 nm center, 10 nm bandwidth), Near-IR (840 nm center, 40 nm bandwidth). The flight experiment at the Guangxi site was performed between 12:30 p.m.–13:30 p.m. on 7 August 2018, covering an area of 21 ha. While the flight experiment at the Hainan site was implemented between 11:00 a.m.–12:00 p.m. on December11, 2018, covering an area of 11 hectares. The flight altitude above ground level was 120 m with an 8 cm ground sample distance (GSD). Then, the original UAV imagery was resampled to generate images with five resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m) by using nearest neighbour resampling algorithm.
In this section, the VIs method was applied to assess the infection status of Fusarium wilt in banana plantations. Eight VIs that related to plant growth and pigment absorption were selected to characterise the biophysical and biochemical variations due to individual infections. These VIs included the NDVI, normalised difference red edge index (NDRE), structural independent pigment index (SIPI), red-edge structural independent pigment index (SIPIRE), green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), anthocyanin reflectance index (ARI), and carotenoid index (CARI). Table 1 lists the formulations of the VIs.
VI | Formulation | Sensitive Parameter | Reference |
---|---|---|---|
NDVI | ( | Green biomass, LAI | [30] |
NDRE | ( | Green biomass, LAI | [31] |
SIPI | ( | Leaf pigment content | [32] |
SIPIRE | ( | Leaf pigment content | [33] |
CIgreen | Leaf chlorophyll content | [34] | |
CIRE | Leaf chlorophyll content | [35] | |
ARI | 1/ | Leaf anthocyanin content | [36] |
CARI | Leaf carotenoid content | [37] |
List of the VIs used in this chapter.
The binary logistic regression (BLR) was used to established the relationships between the VIs and the plants infected or uninfected with Fusarium wilt. As one of the most common multivariate analysis methods, BLR has a dependent variable as a binary variable that represents the presence or absence of an event. The BLR dependent variable is a probability function, which can be expressed as [38]:
where
where
Following the model fitting, the validation datasets were used to verify the accuracy of Fusarium wilt identification models, with indicators of the Kappa coefficient and overall accuracy (OA) [39, 40]. The Kappa coefficient ranges between −1 and 1, kappa ≥0.75 represents excellent agreement, 0.75 > kappa ≥0.4 represents fair to good agreement, kappa <0.4 represents poor represents [41]. The OA is the sum of the correctly identified plots divided by the total number of plots.
Table 2 shows the VI values of the diseased and healthy sample plots. Significant differences (independent
Experiment Site | VI | Sample plot | Mean | Std. Deviation | |
---|---|---|---|---|---|
Guangxi site | NDVI | Healthy | 0.54 | 0.11 | 0.00 |
Diseased | 0.34 | 0.14 | |||
NDRE | Healthy | 0.20 | 0.08 | 0.00 | |
Diseased | 0.02 | 0.09 | |||
SIPI | Healthy | 0.88 | 0.36 | 0.24 | |
Diseased | 1.68 | 5.26 | |||
SIPIRE | Healthy | 0.58 | 0.71 | 0.25 | |
Diseased | 2.07 | 9.77 | |||
CIgreen | Healthy | 1.08 | 0.32 | 0.00 | |
Diseased | 0.43 | 0.33 | |||
CIRE | Healthy | 0.56 | 0.22 | 0.00 | |
Diseased | 0.09 | 0.22 | |||
ARI | Healthy | 0.85 | 0.15 | 0.00 | |
Diseased | 0.62 | 0.16 | |||
CARI | Healthy | 0.34 | 0.04 | 0.00 | |
Diseased | 0.30 | 0.06 | |||
Hainan site | NDVI | Healthy | 0.44 | 0.05 | 0.00 |
Diseased | 0.36 | 0.06 | |||
NDRE | Healthy | 0.35 | 0.10 | 0.00 | |
Diseased | 0.12 | 0.09 | |||
SIPI | Healthy | 1.07 | 0.07 | 0.06 | |
Diseased | 1.18 | 0.12 | |||
SIPIRE | Healthy | 1.11 | 0.11 | 0.04 | |
Diseased | 1.23 | 0.16 | |||
CIgreen | Healthy | 0.92 | 0.26 | 0.00 | |
Diseased | 0.49 | 0.26 | |||
CIRE | Healthy | 0.35 | 0.10 | 0.00 | |
Diseased | 0.12 | 0.09 | |||
ARI | Healthy | 0.87 | 0.30 | 0.03 | |
Diseased | 0.61 | 0.35 | |||
CARI | Healthy | 0.43 | 0.16 | 0.01 | |
Diseased | 0.33 | 0.19 |
Statistical characteristics of the VI values of the diseased and healthy sample plots.
In this section, the relationships between the VIs and the plants infected or uninfected with Fusarium wilt were described by using the BLR method with dataset MD. The classification accuracy of the relational models was verified via both dataset VD1 and VD2. It was found that the use of the NDVI, NDRE, CIgreen, and CIRE led to relatively good fitting recognition models with the OA values greater than 80% (Table 3). Of all the VIs, CIRE obtained the highest verified OA and Kappa coefficient for both VD1 (91.7% for OA and 0.83 for Kappa) and VD2 (80.0% for OA and 0.59 for Kappa), thereby indicating that CIRE performed best in the identification of Fusarium wilt. It could be seen that those VIs containing red-edge band (e.g., NDRE vs. NDVI and CIRE vs. CIgreen) obtained higher verified OA and Kappa coefficients. Nonetheless, CARI and ARI achieved relatively low verified OA and Kappa coefficients.
VI | Recognition model | Dataset VD1 | Dataset VD2 | ||
---|---|---|---|---|---|
OA (%) | Kappa | OA (%) | Kappa | ||
NDVI | 83.3 | 0.66 | 62.9 | 0.22 | |
NDRE | 87.5 | 0.75 | 65.7 | 0.39 | |
CIgreen | 87.5 | 0.74 | 74.3 | 0.47 | |
CIRE | 91.7 | 0.83 | 80.0 | 0.59 | |
ARI | 83.3 | 0.66 | 68.6 | 0.37 | |
CARI | 66.7 | 0.35 | 60.0 | 0.21 |
Recognition models of banana fusarium wilt for different VIs.
Evaluating the impact of image resolutions on the accuracy of Fusarium wilt recognition can provide guidance for the large-scale application of satellite-based data. In this chapter, the original UAV images were first resampled to five different spatial resolutions (0.5-m, 1-m, 2-m, 5-m, and 10-m), which were then used for Fusarium wilt monitoring. We calculated both the optimal VI without a red-edge band (CIgreen) and optimal VI with a red-edge band (CIRE) at different resolutions. Table 4 lists the results of Fusarium wilt recognition model for the CIgreen and CIRE VIs at different resolutions. As indicated by the verified results, the CIRE at resolution 0.5-m, 1-m, and 2-m were all obtained the acceptable verified OA (over 70%) and Kappa coefficients (over 0.40). When using the dataset VD1, the verified OA at resolution 0.5-m, 1-m, and 2-m were 91.7%, 79.2%, and 75.0%, respectively, and the Kappa coefficients were 0.83, 0.60, and 0.53, respectively. When using dataset VD2, the verified OA at resolution 0.5-m, 1-m, and 2-m were 85.7%, 74.3%, and 71.4%, respectively, and the Kappa coefficients were 0.71, 0.48, and 0.41, respectively. Despite that, the OA and Kappa coefficients at resolution 5-m and 10-m resolution were relatively low, and their values dropped as the resolution decreased. Moreover, at the same resolution, the accuracy of the CIgreen-based model for Fusarium wilt recognition was lower than that of CIRE-based model. In fact, the only acceptable result for the CIgreen was at 0.5-m resolution.
Resolution | Recognition model | Dataset VD1 | Dataset VD2 | ||
---|---|---|---|---|---|
OA (%) | Kappa | OA (%) | Kappa | ||
CIRE | |||||
0.5-m | y = 1.987–5.826 × CIRE | 91.7 | 0.83 | 85.7 | 0.71 |
1-m | y = 1.645–4.896 × CIRE | 79.2 | 0.60 | 74.3 | 0.48 |
2-m | y = 1.475–4.178 × CIRE | 75.0 | 0.53 | 71.4 | 0.41 |
5-m | y = 1.027–2.854 × CIRE | 70.8 | 0.42 | 65.7 | 0.30 |
10-m | y = 0.761–1.817 × CIRE | 62.5 | 0.25 | 62.9 | 0.24 |
CIgreen | |||||
0.5-m | y = 3.166–3.946 × CIgreen | 87.5 | 0.75 | 74.3 | 0.48 |
1-m | y = 2.633–3.266 × CIgreen | 75.0 | 0.51 | 65.7 | 0.32 |
2-m | y = 2.421–2.936 × CIgreen | 75.0 | 0.51 | 62.9 | 0.26 |
5-m | y = 1.552–1.862 × CIgreen | 66.7 | 0.35 | 48.6 | 0.01 |
10-m | y = 1.044–1.158 × CIgreen | 58.3 | 0.18 | 45.7 | −0.01 |
Recognition models of banana fusarium wilt for the CIRE and CIgreen at different resolutions.
With the aim to further explore the visual effects of image resolutions, the distribution of Fusarium wilt infected and uninfected areas at the Guangxi site were mapped using different resolution images. CIRE-based and CIgreen-based Fusarium wilt identification models were respectively used to create the Fusarium wilt distribution maps. As can be seen in Figures 2 and 3, the maps with 0.08-m, 0.5-m, 1-m and 2-m resolution show quite similar distributions of the occurrence of Fusarium wilt; however, the maps with 5-m and 10-m resolutions exhibited very little detail. Table 5 lists the area and percentage of the areas infected with Fusarium wilt at different resolutions. For the maps based on CIRE models, the total areas of Fusarium wilt were between 5.69 ha and 6.59 ha, accounting for 38.2% and 44.3% of the banana plantation area. Taking a map with a resolution of 2 m as an example, the incidence of Fusarium wilt is between 40.8% and 43.6%. For the maps based on CIgreen models, the total areas of Fusarium wilt were between 5.09 ha and 6.63 ha, accounting for 34.2% and 44.6% of the banana plantation area. Among them, the percentages of Fusarium wilt of the 0.08-m and 0.5-m resolution maps were 40.1% and 44.6%, respectively.
Maps of the distribution of fusarium wilt based on the CIRE with different resolution images at the Guangxi site.
Maps of the distribution of fusarium wilt based on the CIgreen with different resolution images at the Guangxi site.
Resolution | Diseased area (ha) | Proportion of diseased area (%) |
---|---|---|
CIRE | ||
0.08-m | 6.04 | 40.8 |
0.5-m | 6.59 | 44.3 |
1-m | 6.28 | 42.2 |
2-m | 6.47 | 43.6 |
5-m | 5.70 | 38.5 |
10-m | 5.69 | 38.2 |
CIgreen | ||
0.08-m | 5.95 | 40.1 |
0.5-m | 6.63 | 44.6 |
1-m | 6.44 | 43.3 |
2-m | 6.63 | 44.6 |
5-m | 5.69 | 38.4 |
10-m | 5.09 | 34.2 |
Areas of fusarium wilt based on the CIRE and CIgreen with different resolution images at the Guangxi site.
It was found that among all the VIs used in this chapter, CIRE was the best red-edge VI and CIgreen was the best non-red-edge VI for Fusarium wilt identification. This is because these two VIs are sensitive to the changes of chlorophyll content of a plant, and Fusarium wilt infection in banana will cause a decrease in leaf chlorophyll content [34, 35, 42]. Furthermore, compared with VIs without the red-edge band, VIs with the red-edge band had higher OA and Kappa coefficients (e.g., NDRE vs. NDVI, and CIRE vs. CIgreen). It has been widely proved that the red-edge position is very sensitive to the changes of the plant chlorophyll content [43, 44]. Nevertheless, the UAV-based multispectral imagery used in this chapter only possessed 5 bands, which still cannot fully characterise the differences of the spectral characteristics between the diseased and healthy plants. It is therefore of great significance to use hyperspectral data to further study the sensitivity of certain wavebands to banana Fusarium wilt.
The results also showed the potential of combining BLR and VIs to accurately identify Fusarium wilt of banana. Based on this method, an ideal framework for the use of spectral features can be obtained, so as to clarify the pathological mechanisms. In this chapter, the dependent variable was the occurrence of banana Fusarium wilt. Under the circumstance that the predicted variable has a binary nature, BLR can be regarded as a suitable approach [38]. In addition, BLR can deliver better performance than discriminant analysis in the case that the predictor variables are continuous, categorical, or a combination of the two [45]. BLR is highly interpretable, very efficient, and does not require large computational resources, so it is widely used to describe the relationship between a dependent variable and multiple independent variables [38]. Moreover, due to its linear decision surface, non-linear problems cannot be solved by the logistic regression. With the development of artificial intelligence, pattern recognition and machine learning methods will become more common in the use of remote sensing to monitor and predict plant diseases [46].
The Fusarium wilt detection models were verified both using the dataset VD1 VD2. It can be seen from the verification results that both CIRE and CIgreen performed well in the identification of Fusarium wilt (OA > 70%, and Kappa values >0.4). This indicates that the detection models of Fusarium wilt have a good transferability in other fields. Tables 3 and 4 show that the Kappa coefficients of the dataset VD2 were lower than those of the dataset VD1, thus indicating that applying the detection methodology of Fusarium wilt in other fields would cause some precision loss. This situation may be due to the following factors. First of all, one of the most important factors affecting the verification results could be the fact that there were two different banana varieties at the experimental sites (“Williams B6” in VD1 and “Baxijiao” in VD2). These showed that there were differences in their biophysical and biochemical characteristics, which may cause differences in spectral characteristic information. Secondly, due to the differences in the planting time and climatic conditions of the two experimental sites, their growth stages differed greatly. In fact, the banana plants of two experimental areas were at different growth stages during the investigation. Moreover, soil types, planting density, and environmental conditions for crop growth are also important factors that affect the applicability of the Fusarium wilt identification model. Therefore, it is recommended to appropriately optimise the BLR parameters when applying this method in other regions.
In this chapter, the original UAV images were resampled to generate five resolution images (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m) to evaluate the impact of different resolutions on the accuracy of Fusarium wilt monitoring. It was found that imagery with a resolution smaller than 2 meters had a good accuracy for Fusarium wilt monitoring, which may be related to the planting spacing and the canopy size of banana. With the reduction of the resolution, the mixed pixel problem influences the precision of object recognition and classification. However, image resolution is not the only difference seen between UAV-based and satellite-based sensors. The wavelength information captured by the satellite-based sensor is different from that of UAV-based sensors. Thus, the simulation results at different resolutions should be further verified with actual satellite-based data. In this chapter, single-period multispectral images were used, which limits the spectral response mechanism to determine the changes in the biophysical and chemical parameters caused by Fusarium wilt. In order to overcome this problem, it is necessary to use multi-temporal and hyperspectral images for dynamic monitoring of the occurrence of Fusarium wilt. Additionally, it is also of great value to explore the differences in the spectral response characteristics of Fusarium wilt and other yellowing stresses (i.e., nutrition deficiency and drought stress).
This research used UAV multispectral images to develop a method for identifying Fusarium wilt of banana. The results revealed that the VIs method with BLR analysis can well identify Fusarium wilt. of all the VIs investigated, the CIRE exhibited the optimal performance, with the OA and Kappa coefficients of 91.7% and 0.83 for dataset VD1 and 80.0% and 0.59 for dataset VD2. VIs that included a red edge band obtained better results than those that did not have one. According to the analysis of different resolutions, a resolution smaller than 2 m produced a good identification accuracy of Fusarium wilt. As the resolution decreased however, the identification accuracy decreased. The results indicate that UAV-based multispectral imagery can be applied to identify Fusarium wilt of banana, thus providing reference for disease treatment and crop planting adjustments.
This research was funded by Hainan Provincial Major Science and Technology Program of China (ZDKJ2019006); Youth Innovation Promotion Association CAS (2021119); Future Star Talent Program of Aerospace Information Research Institute, Chinese Academy of Sciences (2020KTYWLZX08); National special support program for high-level personnel recruitment (Wenjiang Huang).
The authors declare no conflict of interest.
We gratefully acknowledge the National Meteorological Information Center of China, Guangxi Jiejiarun Technology Co., Ltd. and Guangxi Jinsui Agriculture Group Co., Ltd. for the experiments.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. 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These data were used as the basis for the development of a mathematical model for the Moon representing its motion over an interval of 100 million years. A program of exploration of the Moon with the aim of creating a permanent base on it is outlined. Such a base is intended for exploring the Earth, the Sun, and outer space.",book:{id:"10955",title:"Lunar Science - Habitat and Humans",coverURL:"https://cdn.intechopen.com/books/images_new/10955.jpg"},signatures:"Joseph J. Smulsky"},{id:"80217",title:"Educational and Scientific Analog Space Missions",slug:"educational-and-scientific-analog-space-missions",totalDownloads:88,totalDimensionsCites:0,doi:"10.5772/intechopen.101392",abstract:"Analog space missions in Poland include international scientific, technological, and business projects designed and realized by a private research company Analog Astronaut Training Center Ltd. (AATC) devoted to the future Moon and Mars exploration. Growing experience in educational aspect of the training as well as continuous development of the habitat and its professional space science laboratory equipment correspond to increased interest of educational organizations, universities, and individual students. We serve unique practical platform for space engineering, space master, and even space doctoral theses. In addition to a wide range of training courses offered for future astronauts, for example, diving, skydiving, rocket workshops, and stratospheric missions, AATC provides a private laboratory to simulate the space environment. It carries out scientific experiments focused on biology and space medicine, as well as addressing several multidisciplinary issues related to the Moon and Mars exploration, including space mining. The main goal of each our analog simulation is to get publishable results, what means that our analog astronauts obtain not only certification of completion of the training but also ability to continue studies and to perform it individually. This chapter summarizes methodology used by us, didactic tools, and obtained results for both educational and scientific analog simulations.",book:{id:"10955",title:"Lunar Science - Habitat and Humans",coverURL:"https://cdn.intechopen.com/books/images_new/10955.jpg"},signatures:"Agata Maria Kołodziejczyk and M. Harasymczuk"},{id:"79544",title:"Regolith and Radiation: The Cosmic Battle",slug:"regolith-and-radiation-the-cosmic-battle",totalDownloads:126,totalDimensionsCites:0,doi:"10.5772/intechopen.101437",abstract:"This chapter discusses regolith utilization in habitat construction mainly from the point of view of radiation protection of humans on missions of long duration. 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