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Stress",doi:"10.5772/intechopen.93709",slug:"plant-growth-and-morphophysiological-modifications-in-perennial-ryegrass-under-environmental-stress",body:'Urban green areas have important various functions contributing to the quality of human health. Well-kept lawns enhance the esthetic value of the entire city and are involved in phytoremediation, leading to an improvement in the quality of the air and soil [1, 2, 3, 4]. Perennial ryegrass (
In fields, the growth and development process of plants needs to counteract various environmental stresses such as salinity, drought, cold, heat, and heavy metal [11, 12, 13]. Harsh environmental conditions may result in growth inhibition, cell structure damage, and metabolic dysfunction [14, 15, 16, 17, 18, 19, 20]. Moreover, stresses will further be intensified for the potential impact of climate change in future. Thus, maintaining proper growth of turfgrass with minimal inputs under abiotic stress conditions is a great challenge for turfgrass industry. This challenge could be addressed through improving the stress tolerance of turfgrass [14, 21]. Understanding morphological and physiological mechanisms of turfgrass adaptation to various abiotic stresses is a key step for the development of stress-tolerant ability and cost-effective and efficient management practices [13]. Morphophysiological mechanisms of turfgrass in abiotic stresses tolerance involve phenotypic changes, multiple physiological and biochemical response, and complex metabolic processes, such as water and nutrient relations, carbohydrate metabolism, protein metabolism, hormone metabolism, as well as antioxidant defenses [22, 23]. Current studies on morphophysiological mechanism controlling turfgrass adaptations to various growth conditions have provided important information for production of abiotic stress-tolerant germplasms and the further understanding of regulation mechanism of turfgrass response to abiotic stresses [13, 24, 25]. However, the mechanisms of the adaptive responses are integrated but are not necessarily the same [14]; thus, studies on how perennial ryegrass adapts to stress conditions will become more important with the increasing pressure of utilizing both ecological and economical strategies in the turf management. Furthermore, insights into mechanisms of stress resistance in perennial ryegrass will aid in identifying important characteristics for selecting the criteria of improving stress tolerance and will ultimately lead to better selection of new cultivars adapted to adverse environments. This chapter, therefore, focuses on an extensive overview of the current understanding of changes in physiology and growth/development of perennial ryegrass under various abiotic stresses. In addition, strategies for improving the stress tolerance of perennial ryegrass are also presented. This review can contribute to the better understanding of the mechanisms of perennial ryegrass response to environmental stresses and can provide valuable information for improving resistance characteristics of perennial ryegrass by breeding. Moreover, enhancing our understanding of physiological effects of abiotic stresses can provide guidelines for the practical management strategies of the maintenance of high-quality turf under limited resource availability.
Abiotic stresses are major environmental conditions that reduce plant growth, productivity, and quality. Plants have evolved mechanisms to perceive these environmental challenges, transmit the stress signals within cells as well as between cells and tissues, and make appropriate adjustments in their growth and development for survive and reproduce [26, 27, 28, 29]. The morphological and physiological changes of perennial ryegrass under abiotic stress will be discussed in this chapter.
Growth and development processes are inhibited when plant is exposed to drought stress [30, 31, 32, 33, 34, 35, 36]. Morphological adjustments, such as biomass allocation and leave changes, have been proposed as the key mechanisms used by turfgrass to enhance survival under drought [37]. There is a series of morphology changes in perennial ryegrass under drought stress. Drought stress reduced the turf quality (TQ), number of live tillers and dry-matter yield [38, 39, 40]. Moreover, drought significantly enhanced root to shoot ratio (R/S) in perennial ryegrass to an less extents, depending on the intensity, the reason may be that perennial ryegrass in drought stress develop a large R/S to maintain water and nutrient uptake [39]. The leaves of perennial ryegrass under drought stress were also dramatically different from that of nonstressed perennial ryegrass, for example, under drought stress, the diurnal variation in the rate of leaf extension was smaller but the leaves tended to grow faster at night compared to normal irrigation controls; however, water stress ultimately reduced the rate of leaf extension and leaf area in perennial ryegrass [40]. Furthermore, the leaves’ epidermis of perennial ryegrass under drought reduced the stomatal size and increased the numbers per unit leaf area. Drought also resulted deeper ridging on leaf ad-axial surface, smaller epidermal cells and bigger ridge angle [40]. Under drought stress, leave stomata of perennial ryegrass began to close to reduce their evapotranspiration rate (ET), at leaf water potentials below—13 bars [40].
Drought stress causes significant physiological changes, including photosynthesis, osmotic adjustment substances, proteins, and antioxidant metabolism, in perennial ryegrass. For instance, the content of leaf total nitrogen and leaf relative water content (RWC) were tested to decrease, on the contrary, antioxidant activity including ascorbate peroxidase (APX), superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), glutathione S-transferase (GST), amino acids such as aspartic acid, threonine, serine, glutamic acid, abscisic acid (ABA) concentration, and proline content increased under drought stress [41]. Photosynthesis is the primary process controlling plant growth and adaption to drought stress [42]. The canopy photosynthesis of perennial ryegrass at saturating light intensity was reduced by about half in the stressed field swards and by more than 80% in the stressed simulated swards [38]. Drought stress inhibits photosynthesis, which may be the result of low CO2 availability caused by stomatal closure and/or the inhibition of photochemical reactions and carbon assimilation metabolism [43]. In addition to the photosynthesis, starchis also considered as a buffer for imbalance between acquisition by photosynthesis and C-sink activities such as growth and respiration resulted from drought, also stress due to the excessive use of inorganic fertilizer [44]. However, use of green manure also has risk of xenobiotic contamination [45, 46, 47], and the soluble sugars, including sucrose, fructose and glucose, are involved in multiple physiological functions such as respiration, turgor maintenance, signaling and defense. Under drought conditions, starch of perennial ryegrass significantly decreased in shoots, but did not change in roots, which indicated that perennial ryegrass in drought condition preferentially allocates carbon not only to root growth, but also to root storage, while soluble sugars were enhanced in both shoots and roots. Accumulation of soluble sugars has been widely reported for plants upon water stress as a means to provide osmotic protection [39], which suggested that increasing of soluble sugars was benefit to plants to maintain growth and active metabolic activities under water deficit.
It is generally accepted that there is a noticeable genotypic variation in perennial ryegrass for drought stress responses. The research showed that one self-pollinating genotype “S10” showed higher RWC, shoot dry weight (SDW), proline, ABA, nitrogen and amino acid contents, and antioxidant enzymes activities in comparison with two commercial genotypes of “Vigor” and “Speedy” [41]. Proteins involved in carbon and energy metabolism, photosynthesis, tricarboxylic acid cycle (TCA) cycle, redox, and transport categories were upregulated in the two commercial genotypes of “Vigo” and “Speedy,” while the protein profile of the “S10” changed slightly under drought stress, and the reason may be that self-pollination in the genetic background of the “S10” genotype may have a lower variation in response to drought stress conditions [41]. Additionally, other research indicated that tetraploid perennial ryegrass exhibited a greater biomass under severe drought, whereas diploids had a greater biomass under the current rainfall [48, 49]. Moreover, tetraploid perennial ryegrass populations were able to develop more shoot and root dry matter than diploid populations in following the application of drought stress [50].
The above researches showed that drought stress caused significantly physiological and morphological changes in perennial ryegrass (Table 1) [55, 56]. Thus, the growth of perennial ryegrass is severely restricted by soil water deficits [57]. Increasing drought tolerance of perennial ryegrass via strategies is importance for both water conservation and maintaining growth in water limiting environments. For example, the grass-Epichloë endophytic improved water utilization and drought tolerance in perennial ryegrass [58]. Moreover, arbuscular mycorrhizal fungi (AMF) + Epichloë treatments increased phosphorus (P) uptake, net photosynthetic rate (Pn), root activity, soluble sugar concentration, peroxidase (POD) activity, and decreased malonyldialdehyde (MDA) concentration in perennial ryegrass under drought stress, the reason may be that Plant-AMF-Epichloë symbiosis alleviated the damage caused by drought stress by promoting P uptake, photosynthesis, and the accumulation of osmoregulatory substances [59]. Additionally, application of plant growth regulators (PGRs) have been reported to be a promising way of reducing drought stress impacts [60]. The study manifested that trinexapac ethyl (TE) treatment increased chlorophyll content, proline content, the RWC, soluble sugar content, antioxidant enzymes activities, decreased MDA and hydrogen peroxide (H2O2) contents in perennial ryegrass under drought stress, while Paclobutrazol (PAC)- and ABA-treated perennial ryegrasses were all effective in mitigating physiological damages resulting from drought stress [52]. Furthermore, overexpression of some drought-related genes has been shown to effectively improve drought tolerance of plants [61]. According to Patel et al. [53], overexpression of
Morphological responses | Physiological responses | Strategies |
---|---|---|
• Decreased turf quality • Enhanced R/S • Decreased leaf area • Reduce the number of live tillers • Had smaller stomata and epidermal cells • Had bigger ridge angle • Controlled stomatal opening [38, 39, 40] | • Decline biomass • Decline photosynthetic rate • Increased osmotic adjustment substances • Increased antioxidant activity • Increased amino acid content [39, 41, 51] | • Application of plant growth regulators (PGRs) • Selected drought resistance cultivars from different cultivars • Using endophytes • Using transgenic technology [52, 53, 54] |
Morphophysiological response of perennial ryegrass under drought stress.
Perennial ryegrass can grow throughout the year, and the major constraint on growth is temperature [51, 62]. Perennial ryegrass has an optimal growth temperature of about 20°C, and it is sensitive to high (30–40°C) and low (−20 to 0°C) temperatures [63, 64]. Common perennial ryegrass germinates quickly and can be used as a temporary ground cover while the slower growing bluegrass plants take hold in cool temperate region. In warm climates, it is used as an overseed to maintain winter green in the lawn after the warm season grasses go dormant. However, populations of perennial ryegrass will not survive the summer heat. Severe heat stress (40/35°C day/night) caused significant physiological damages, including declining in TQ , RWC, CAT activity, and enhancing in electrolyte leakage (EL) of leaves and MDA content, in perennial ryegrass [65]. Moreover, heat stress decreased plant height (HT), leaf fresh weight (LFW) and leaf fresh dry (LFD), and increased cytokinin and auxin at 35/30°C (day/night) of temperature [66]. Moreover, low temperature is one of the main factors that limit the persistence of perennial ryegrass-dominated grasslands in northern regions. Cold stress decreased TQ , regrowth, dry weight, and tiller density in perennial ryegrass when the winters were mild with short (2–6 weeks) periods of lower than −10°C temperatures and no permanent snow cover [67]. Furthermore, cold stress decreased RWC and increase EL in leaves and roots when perennial ryegrass was exposed to −15 or −25°C [68]. To resolve these problems and maintain high visual quality of perennial ryegrass through the year, it is important to found new cultivars to adapt to temperature stress. Variations of heat- or cold- resistance were also found among different perennial ryegrass cultivars. Thus, selection cold- or heat-tolerant cultivars during perennial ryegrass genotypes can be an effective method for temperature tolerance improvement in perennial ryegrass. The research tested the heat tolerance of 58 cultivars collected from seed companies and research centers in U.S.A., New Zealand, and Europe, the result showed that distinct heat tolerance was found among the cultivars at all the temperature regimes, and the least and most tolerant cultivars were “JPR005” and “JPR178,” respectively [69]. The other research indicated that changes of morphology and physiology were different for heat-tolerant accession “PI265351” and sensitive accession “PI225825” [66]. Similarly, the heat-tolerant populations of perennial grass showed significantly lower degree damage in efficiency of photosystem II and cell membrane stability than the sensitive ones at different levels of stress [70]. Additionally, the study showed that 21 accessions sampled from a larger set of 300 accessions with known winter hardiness, the result showed that the degree of semi-lethal temperature in 21 ryegrass varieties varies from −10.31 to −13.95°C, with 3 accessions possessing significantly greater freezing tolerance than the most freeze-tolerant check “NK200” [71]. Moreover, tetraploid genotypes of perennial ryegrass demonstrated higher tolerance to cold stress conditions, better spring growth, and regrowth after cuts, and higher dry matter yield compared to diploid genotypes [67].
The studies indicate that temperature stress caused the morphological and physiological damage in plant, and the response of genotypes to temperature stress was different [72, 73, 74, 75, 76, 77, 78, 79, 80]. Therefore, founding some strategies which could improve cold- or heat-tolerant of perennial ryegrass is important. It was also reported that 24-epibrassinolide promoted carbohydrates accumulation in crowns of perennial ryegrass during cold acclimation by regulation of gene expression and enzyme activities, and which resulted in increased frost tolerance [81]. Moreover, drought preconditioning increased in crown fructans, proline, and total soluble protein content for “Buccaneer” and “Sunkissed” during cold acclimation, which suggested a synergistic effect between drought exposure and low temperature, and drought preconditioning resulted in an improvement in freezing tolerance of perennial ryegrass [82]. Additionally, previous studies have shown that the enzyme activity level and gene expression of antioxidants are associated with cold and heat tolerance in a cool-season perennial grass species [83, 84]. For instance,
Salinity stress has become a more significant problem in turfgrass management in many areas [13]. Responses of plants to salinity stress occur mainly through two distinct phases over time: osmotic-changing and ion specific phases [87, 88, 89]. Like other turfgrasses, salt stress caused morphology, physiology, molecular changes in growth and development of perennial ryegrass, such as TQ LFW, LED, and RWC of perennial ryegrass decreasing after exposure to salinity [89, 90]. The alterations of morphological characteristics of turfgrass under salt stress are derived from the changes of physiological traits such as cell membrane stability [14]. It was reported that MDA content and EL enhanced by NaCl concentration in perennial ryegrass [54]. Simultaneously, superoxide radical (O2−), H2O2, and singlet oxygen (O2) concentration increased observably in perennial ryegrass after salt stress treatment [54, 91]. To scavenge reactive oxygen species (ROS), salt-stressed leaves of perennial ryegrass exhibited greater activities of SOD, APX, and CAT at the initial stage of salt stress, but lower levels of enzyme with the extension of salt stress [89]. Salt stress also negatively affected on the total chlorophyll (Chl), Chl a and Chl b, in perennial ryegrass [89], which showed that salt stress induced Chl decomposition in leaves. Moreover, a further research of PSII changes in perennial ryegrass discovered that quantum yields, efficiencies, and energy fluxes were impacted after salt stress treatment [92, 93]. Additionally, a vast amount of Na+ accumulated in plants could induce ionic imbalance in the cells. It was reported that Na+ concentration accumulated rapidly and other ion concentrations including K+, Ca2+ and Mg2+ were decreased in response to salt stress in perennial ryegrass [89].
Salt stress causes dramatically changes in morphology and physiology of perennial ryegrass as showed above and summaries in Figure 1. However, these responses varied greatly among different genotypes. The research compared the salt tolerance in 10 accessions of perennial ryegrass, and determined that “PI275660” and “BrightStar” showed the best tolerance to salt stress, while “PI231595” and “PI251141” were the most sensitive accessions [5]. The other research reported that the effect on parameters of photosynthetic efficiency in perennial ryegrass “Roadrunner” was less than that in “Nira” under salt stress condition [6]. Moreover, the highest salt tolerance accessions were from the European group, wild accessions and exhibited more variation in functional traits and salt tolerance than commercial cultivars [90]. Some other strategies can also improve the salt-tolerance in perennial ryegrass. Salt-tolerant transgenic perennial ryegrass could be obtained by
Morphophysiological response and strategies for salt stress in perennial ryegrass.
The continuing industrialization has led to extensive environmental problems worldwide [96, 97, 98]. Heavy metals produced from industry are released to soil. Thus, high accumulation of heavy metal in soil can induce environmental stress on plants [14]. Research on the response of perennial ryegrass to heavy metal stress has also progressed in recent years. It has been proved that heavy metals can induce damage and affect metabolic processes in perennial ryegrass [98, 99, 100]. For example, perennial ryegrass had characters in yield reduction and visible symptoms of phytotoxicity under cadmium (Cd) and zinc (Zn) stress [98]. Moreover, the cellular membrane system was damaged because of elevated MDA and EL contents when perennial ryegrass was exposed to salt condition [101]. According to studies, a dramatic inhibition of root and shoot growth was detected in perennial ryegrass after heavy metals treatment [101, 102, 103]. Moreover, the composition of the leaves of perennial ryegrass, including apparently opposite effects on the calcium (Ca), potassium (K) and P levels, was changed under the aluminum (Al) stress [104]. Additionally, ROS bursts occurred in perennial ryegrass under heavy mental stress conditions. For instance, H2O2 and O2− were significantly accumulated in perennial ryegrass under Cd stress [105]. Hence, the protection mechanisms in perennial ryegrass such as the antioxidant system were triggered under heavy stress, resulting in the increase of SOD, CAT, and POD activities and their corresponding genes [106]. Moreover, content of fructan, sugar, and starch showed an increasing trend in perennial ryegrass after heavy metal stress [98]. However, certain concentrations of heavy metal were beneficial for the growth of perennial ryegrass [107]. Heavy metal stresses not only induce physiological damage, but also inhibit germination and growth of perennial ryegrass [108].
To improve the heavy metal stress tolerance of perennial ryegrass, several investigations were conducted in recent years. It was reported that signal messengers such as nitricoxide (NO) and glycine betaine (GB) play crucial roles in alleviating Cd and Cu-induced damages in perennial ryegrass [109, 110]. Moreover, the exogenous P was testified to improve the Cd tolerance of perennial ryegrass, the reason may be that exogenous P facilitates chelation-mediated Cd detoxification processes [105]. Similarly, a high dose of P amendment alleviated Mn-toxicity in Mn-sensitive genotype in perennial ryegrass [102]. Furthermore, the addition of biochar to a contaminated mine soil improved the nutrient status of this mine soil and contributed to a better establishment of perennial ryegrass [100]. Additionally, AMF enhance both absorption and stabilization of Cd by perennial ryegrass in a Cd-contaminated acidic soil [96], and ethylene diamine tetra acetate (EDTA) enhanced phytoremediation of heavy metals from municipal waste compost and sludge soil by perennial ryegrass [99, 111, 112].
Significant progress has been made in the understanding of morphological and physiological mechanisms associated with perennial ryegrass tolerance to drought, salinity, temperature, and heavy mental stresses. Harsh stress conditions inhibit the growth and development and decrease TQ , root length, and dry weight in perennial ryegrass. Moreover, physiological response to abiotic stress in perennial ryegrass displays changes of the cell membrane, photosystem, metabolites, and antioxidant system. The contents of MDA and EL are increased, while Chl content and photosynthesis are decreased under stress conditions. To regulate the osmotic potential of the cell after stress treatment, some metabolites such as proline, soluble sugars, and proteins accumulate. Meanwhile, antioxidant enzymes’ activities increase in perennial ryegrass for scavenging ROS. Perennial ryegrass has protective responses against unfavorable conditions, but there is a threshold to these physiological changes. To understand the response to abiotic stress and resistance attributes in perennial ryegrass will be beneficial to breeding in future.
For improving the stress tolerance of perennial ryegrass, some practical strategies are exploited currently, such as application of phytohormones, endophytes, and chemical compounds. Further research on increasing perennial ryegrass stress tolerance should pay more attention to transgenic technology to identify effective genes for modifying stress-tolerance ability.
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In the field of remote sensing, HSI classification has been an established research topic, and herein, the inherent primary challenges are (i) curse of dimensionality and (ii) insufficient samples pool during training. Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel. This chapter discusses the recent progress in the classification of HS images in the aspects of Kernel-based methods, supervised and unsupervised classifiers, classification based on sparse representation, and spectral-spatial classification. Further, the classification methods based on machine learning and the future directions are discussed.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Rajesh Gogineni and Ashvini Chaturvedi",authors:null},{id:"68884",doi:"10.5772/intechopen.88910",title:"Hyperspectral Endmember Extraction Techniques",slug:"hyperspectral-endmember-extraction-techniques",totalDownloads:1154,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Hyperspectral data processing and analysis mainly plays a vital role in detection, identification, discrimination and estimation of earth surface materials. It involves atmospheric correction, dimensionality reduction, endmember extraction, spectral unmixing and classification phases. One of the ultimate aims of hyperspectral data processing and analysis is to achieve high classification accuracy. The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. This chapter gives and highlights the generic approach of endmember extraction with popular algorithm limitations and challenges.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Karbhari V. Kale, Mahesh M. Solankar and Dhananjay B. Nalawade",authors:null},{id:"66838",doi:"10.5772/intechopen.86095",title:"NIR Hyperspectral Imaging for Mapping of Moisture Content Distribution in Tea Buds during Dehydration",slug:"nir-hyperspectral-imaging-for-mapping-of-moisture-content-distribution-in-tea-buds-during-dehydratio",totalDownloads:866,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This work employed hyperspectral imaging technique to map the spatial distribution of moisture content (MC) in tea buds during dehydration. Hyperspectral images (874–1734 nm) of tea buds were acquired in six dehydrated periods (0, 3, 6, 9, 14 and 21 min) at 80°C. The spectral reflectance of tea buds were extracted from region of interests (ROIs) in the hyperspectral images. Competitive adaptive reweighted sampling (CARS) was used to select effective wavelengths (EWs) and ten representing the wavelengths were selected. The quantitative relationship between spectral reflectance and the measured MC values of tea buds was built using partial least square regression (PLSR) based on full spectra and EWs. The quantitative model established using EWs, which had a result of coefficient of correlation (RP) of 0.941 and root mean square error of prediction (RMSEP) of 5.31%, was considered as the optimal model for mapping MC distribution. The optimal model was finally applied to predict the MC of each pixel within of the tea bud sample and built the MC distribution maps by utilization of a developed image processing procedure. Results demonstrated that the hyperspectral imaging technique has the potential of mapping the MC spatial distribution in tea buds in dehydrated process.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Keqiang Yu, Yanru Zhao, Xiaoli Li and Yong He",authors:null},{id:"68910",doi:"10.5772/intechopen.88980",title:"Fast Chaotic Encryption for Hyperspectral Images",slug:"fast-chaotic-encryption-for-hyperspectral-images",totalDownloads:746,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The information collected by hyperspectral images (HI) is essential in applications of remote sensing like object detection, geological process recognition, and identifying materials. However, HI information could be sensitive, and therefore, it should be protected. In this chapter, we show a parallel encryption algorithm specifically designed for HI. The algorithm uses multiple chaotic systems to produce a crossed multidimensional chaotic map for encrypting the image; the scheme takes advantage of the multidimensional nature of HI and is highly parallelizable, which leads to a time-efficient algorithm. We also show that the algorithm gets high-entropy ciphertext and is robust to ciphertext-only attacks.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Carlos Villaseñor, Javier Gomez-Avila, Nancy Arana-Daniel, Alma Y. 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The optimization-based approaches estimate HR-HS image via minimizing the reconstruction errors of the available low-resolution hyperspectral and high-resolution multispectral images with different constrained prior knowledge such as representation sparsity, spectral physical properties, spatial smoothness, and so on. Recently, deep convolutional neural network (DCNN) has been applied to resolution enhancement of natural images and is proven to achieve promising performance. This chapter provides a comprehensive description of not only the conventional optimization-based methods but also the recently investigated DCNN-based learning methods for HS image super-resolution, which mainly include spectral reconstruction CNN and spatial and spectral fusion CNN. Experiment results on benchmark datasets have been shown for validating effectiveness of HS image super-resolution in both quantitative values and visual effect.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Xian-Hua Han",authors:null}],mostDownloadedChaptersLast30Days:[{id:"70188",title:"Hyperspectral Image Classification",slug:"hyperspectral-image-classification",totalDownloads:1431,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images. In the field of remote sensing, HSI classification has been an established research topic, and herein, the inherent primary challenges are (i) curse of dimensionality and (ii) insufficient samples pool during training. 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The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. 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This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm. We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscattering of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Mbongowo Mbuh",authors:null},{id:"66838",title:"NIR Hyperspectral Imaging for Mapping of Moisture Content Distribution in Tea Buds during Dehydration",slug:"nir-hyperspectral-imaging-for-mapping-of-moisture-content-distribution-in-tea-buds-during-dehydratio",totalDownloads:862,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This work employed hyperspectral imaging technique to map the spatial distribution of moisture content (MC) in tea buds during dehydration. Hyperspectral images (874–1734 nm) of tea buds were acquired in six dehydrated periods (0, 3, 6, 9, 14 and 21 min) at 80°C. The spectral reflectance of tea buds were extracted from region of interests (ROIs) in the hyperspectral images. Competitive adaptive reweighted sampling (CARS) was used to select effective wavelengths (EWs) and ten representing the wavelengths were selected. The quantitative relationship between spectral reflectance and the measured MC values of tea buds was built using partial least square regression (PLSR) based on full spectra and EWs. The quantitative model established using EWs, which had a result of coefficient of correlation (RP) of 0.941 and root mean square error of prediction (RMSEP) of 5.31%, was considered as the optimal model for mapping MC distribution. The optimal model was finally applied to predict the MC of each pixel within of the tea bud sample and built the MC distribution maps by utilization of a developed image processing procedure. Results demonstrated that the hyperspectral imaging technique has the potential of mapping the MC spatial distribution in tea buds in dehydrated process.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Keqiang Yu, Yanru Zhao, Xiaoli Li and Yong He",authors:null},{id:"69170",title:"Hyperspectral Image Super-Resolution Using Optimization and DCNN-Based Methods",slug:"hyperspectral-image-super-resolution-using-optimization-and-dcnn-based-methods",totalDownloads:923,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Reconstructing a high-resolution (HR) hyperspectral (HS) image from the observed low-resolution (LR) hyperspectral image or a high-resolution multispectral (RGB) image obtained using the exiting imaging cameras is an important research topic for capturing comprehensive scene information in both spatial and spectral domains. The HR-HS hyperspectral image reconstruction mainly consists of two research strategies: optimization-based and the deep convolutional neural network-based learning methods. The optimization-based approaches estimate HR-HS image via minimizing the reconstruction errors of the available low-resolution hyperspectral and high-resolution multispectral images with different constrained prior knowledge such as representation sparsity, spectral physical properties, spatial smoothness, and so on. Recently, deep convolutional neural network (DCNN) has been applied to resolution enhancement of natural images and is proven to achieve promising performance. This chapter provides a comprehensive description of not only the conventional optimization-based methods but also the recently investigated DCNN-based learning methods for HS image super-resolution, which mainly include spectral reconstruction CNN and spatial and spectral fusion CNN. Experiment results on benchmark datasets have been shown for validating effectiveness of HS image super-resolution in both quantitative values and visual effect.",book:{id:"8223",slug:"processing-and-analysis-of-hyperspectral-data",title:"Processing and Analysis of Hyperspectral Data",fullTitle:"Processing and Analysis of Hyperspectral Data"},signatures:"Xian-Hua Han",authors:null}],onlineFirstChaptersFilter:{topicId:"639",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:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,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:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:16,numberOfOpenTopics:5,numberOfUpcomingTopics:0,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. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). 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He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. 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