420 research outputs found

    Statistical and image processing techniques for remote sensing in agricultural monitoring and mapping

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    Throughout most of history, increasing agricultural production has been largely driven by expanded land use, and – especially in the 19th and 20th century – by technological innovation in breeding, genetics and agrochemistry as well as intensification through mechanization and industrialization. More recently, information technology, digitalization and automation have started to play a more significant role in achieving higher productivity with lower environmental impact and reduced use of resources. This includes two trends on opposite scales: precision farming applying detailed observations on sub-field level to support local management, and large-scale agricultural monitoring observing regional patterns in plant health and crop productivity to help manage macroeconomic and environmental trends. In both contexts, remote sensing imagery plays a crucial role that is growing due to decreasing costs and increasing accessibility of both data and means of processing and analysis. The large archives of free imagery with global coverage, can be expected to further increase adoption of remote sensing techniques in coming years. This thesis addresses multiple aspects of remote sensing in agriculture by presenting new techniques in three distinct research topics: (1) remote sensing data assimilation in dynamic crop models; (2) agricultural field boundary detection from remote sensing observations; and (3) contour extraction and field polygon creation from remote sensing imagery. These key objectives are achieved through combining methods of probability analysis, uncertainty quantification, evolutionary learning and swarm intelligence, graph theory, image processing, deep learning and feature extraction. Four new techniques have been developed. Firstly, a new data assimilation technique based on statistical distance metrics and probability distribution analysis to achieve a flexible representation of model- and measurement-related uncertainties. Secondly, a method for detecting boundaries of agricultural fields based on remote sensing observations designed to only rely on image-based information in multi-temporal imagery. Thirdly, an improved boundary detection approach based on deep learning techniques and a variety of image features. Fourthly, a new active contours method called Graph-based Growing Contours (GGC) that allows automatized extractionof complex boundary networks from imagery. The new approaches are tested and evaluated on multiple study areas in the states of Schleswig-Holstein, Niedersachsen and Sachsen-Anhalt, Germany, based on combine harvester measurements, cadastral data and manual mappings. All methods were designed with flexibility and applicability in mind. They proved to perform similarly or better than other existing methods and showed potential for large-scale application and their synergetic use. Thanks to low data requirements and flexible use of inputs, their application is neither constrained to the specific applications presented here nor the use of a specific type of sensor or imagery. This flexibility, in theory, enables their use even outside of the field of remote sensing.Landwirtschaftliche ProduktivitĂ€tssteigerung wurde historisch hauptsĂ€chlich durch Erschließung neuer AnbauflĂ€chen und spĂ€ter, insbesondere im 19. und 20. Jahrhundert, durch technologische Innovation in ZĂŒchtung, Genetik und Agrarchemie sowie Intensivierung in Form von Mechanisierung und Industrialisierung erreicht. In jĂŒngerer Vergangenheit spielen jedoch Informationstechnologie, Digitalisierung und Automatisierung zunehmend eine grĂ¶ĂŸere Rolle, um die ProduktivitĂ€t bei reduziertem Umwelteinfluss und Ressourcennutzung weiter zu steigern. Daraus folgen zwei entgegengesetzte Trends: Zum einen Precision Farming, das mithilfe von Detailbeobachtungen die lokale Feldarbeit unterstĂŒtzt, und zum anderen großskalige landwirtschaftliche Beobachtung von Bestands- und Ertragsmustern zur Analyse makroökonomischer und ökologischer Trends. In beiden FĂ€llen spielen Fernerkundungsdaten eine entscheidende Rolle und gewinnen dank sinkender Kosten und zunehmender VerfĂŒgbarkeit, sowohl der Daten als auch der Möglichkeiten zu ihrer Verarbeitung und Analyse, weiter an Bedeutung. Die VerfĂŒgbarkeit großer, freier Archive von globaler Abdeckung werden in den kommenden Jahren voraussichtlich zu einer zunehmenden Verwendung fĂŒhren. Diese Dissertation behandelt mehrere Aspekte der Fernerkundungsanwendung in der Landwirtschaft und prĂ€sentiert neue Methoden zu drei Themenbereichen: (1) Assimilation von Fernerkundungsdaten in dynamischen Agrarmodellen; (2) Erkennung von landwirtschaftlichen Feldgrenzen auf Basis von Fernerkundungsbeobachtungen; und (3) Konturextraktion und Erstellung von Polygonen aus Fernerkundungsaufnahmen. Zur Bearbeitung dieser Zielsetzungen werden verschiedene Techniken aus der Wahrscheinlichkeitsanalyse, Unsicherheitsquantifizierung, dem evolutionĂ€ren Lernen und der Schwarmintelligenz, der Graphentheorie, dem Bereich der Bildverarbeitung, Deep Learning und Feature-Extraktion kombiniert. Es werden vier neue Methoden vorgestellt. Erstens, eine neue Methode zur Datenassimilation basierend auf statistischen Distanzmaßen und Wahrscheinlichkeitsverteilungen zur flexiblen Abbildung von Modell- und Messungenauigkeiten. Zweitens, eine neue Technik zur Erkennung von Feldgrenzen, ausschließlich auf Basis von Bildinformationen aus multi-temporalen Fernerkundungsdaten. Drittens, eine verbesserte Feldgrenzenerkennung basierend auf Deep Learning Methoden und verschiedener Bildmerkmale. Viertens, eine neue Aktive Kontur Methode namens Graph-based Growing Contours (GGC), die es erlaubt, komplexe Netzwerke von Konturen aus Bildern zu extrahieren. Alle neuen AnsĂ€tze werden getestet und evaluiert anhand von MĂ€hdreschermessungen, Katasterdaten und manuellen Kartierungen in verschiedenen Testregionen in den BundeslĂ€ndern Schleswig-Holstein, Niedersachsen und Sachsen-Anhalt. Alle vorgestellten Methoden sind auf FlexibilitĂ€t und Anwendbarkeit ausgelegt. Im Vergleich zu anderen Methoden zeigten sie vergleichbare oder bessere Ergebnisse und verdeutlichten das Potenzial zur großskaligen Anwendung sowie kombinierter Verwendung. Dank der geringen Anforderungen und der flexiblen Verwendung verschiedener Eingangsdaten ist die Nutzung nicht nur auf die hier beschriebenen Anwendungen oder bestimmte Sensoren und Bilddaten beschrĂ€nkt. Diese FlexibilitĂ€t erlaubt theoretisch eine breite Anwendung, auch außerhalb der Fernerkundung

    Automatic Classification of Chickpea Varieties Using Computer Vision Techniques

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    There are about 90 different varieties of chickpeas around the world. In Iran, where this study takes place, there are five species that are the most popular (Adel, Arman, Azad, Bevanij and Hashem), with different properties and prices. However, distinguishing them manually is difficult because they have very similar morphological characteristics. In this research, two different computer vision methods for the classification of the variety of chickpeas are proposed and compared. The images were captured with an industrial camera in Kermanshah, Iran. The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial neural networks and particle swarm optimization (ANN-PSO). The second method is not based on an explicit extraction of features; instead, image patches (RGB pixel values) are directly used as input for a three-layered backpropagation ANN. The first method achieved a correct classification rate (CCR) of 97.0%, while the second approach achieved a CCR of 99.3%. These results prove that visual classification of fruit varieties in agriculture can be done in a very precise way using a suitable method. Although both techniques are feasible, the second method is generic and more easily applicable to other types of crops, since it is not based on a set of given features.This research was funded by the Spanish MICINN, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53. It has also been supported by the European Union (EU) under Erasmus + project entitled "Fostering Internationalization in Agricultural Engineering in Iran and Russia [FARmER]" with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP

    A Cost-effective Multispectral Sensor System for Leaf-Level Physiological Traits

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    With the concern of the global population to reach 9 billion by 2050, ensuring global food security is a prime challenge for the research community. One potential way to tackle this challenge is sustainable intensification; making plant phenotyping a high throughput may go a long way in this respect. Among several other plant phenotyping schemes, leaf-level plant phenotyping needs to be implemented on a large scale using existing technologies. Leaf-level chemical traits, especially macronutrients and water content are important indicators to determine crop’s health. Leaf nitrogen (N) level, is one of the critical macronutrients that carries a lot of worthwhile nutrient information for classifying the plant’s health. Hence, the non-invasive leaf’s N measurement is an innovative technique for monitoring the plant’s health. Several techniques have tried to establish a correlation between the leaf’s chlorophyll content and the N level. However, a recent study showed that the correlation between chlorophyll content and leaf’s N level is profoundly affected by environmental factors. Moreover, it is also mentioned that when the N fertilization is high, chlorophyll becomes saturated. As a result, determining the high levels of N in plants becomes difficult. Moreover, plants need an optimum level of phosphorus (P) for their healthy growth. However, the existing leaf-level P status monitoring methods are expensive, limiting their deployment for the farmers of low resourceful countries. The aim of this thesis is to develop a low-cost, portable, lightweight, multifunctional, and quick-read multispectral sensor system to sense N, P, and water in leaves non-invasively. The proposed system has been developed based on two reflectance-based multispectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). By deploying state of the art machine learning algorithms, the spectroscopic information is modeled and validated to predict that nutrient status. A total of five experiments were conducted including four on the greenhouse-controlled environment and one in the field. Within these five, three experiments were dedicated for N sensing, one for water estimation, and one for P status determination. In the first experiment, spectral data were collected from 87 leaves of canola plants, subjected to varying levels of N fertilization. The second experiment was performed on 1008 leaves from 42 canola cultivars, which were subjected to low and high N levels, used in the field experiment. The K-Nearest Neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the first experiment and 79.2% for the second experiment. In the third and fourth experiments, spectral data were collected from 121 leaves for N and 186 for water experiments respectively; and Rational Quadratic Gaussian Process Regression (GPR) algorithm is applied to correlate the reflectance data with actual N and water content. By performing 5-fold cross-validation, the N estimation shows a coefficient of determination (R^2) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola shows an R^2 of 18.02%, corn of 68.41%, soybean of 46.38%, and wheat of 64.58%. Finally, the fifth experiment was conducted on 267 leaf samples subjected to four levels of P treatments, and KNN exhibits the best accuracy, on the test set, of about 71.2%, 73.5%, and 67.7% for corn, soybean, and wheat, respectively. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf N and P status/content. However, further investigation is needed to improve the water estimation results using the proposed device. Moreover, the utility of the device to estimate other nutrients as well as other crops has great potential for future research

    A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

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    Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.Comment: 44 pages, 9 figure

    Bioinformatics Applications Based On Machine Learning

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    The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

    Discovery of Novel Glycogen Synthase Kinase-3beta Inhibitors: Molecular Modeling, Virtual Screening, and Biological Evaluation

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    Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. Due to its distinct regulation mechanism and unique substrate specificity in the molecular pathogenesis of human diseases, GSK-3 is one of the most attractive therapeutic targets for the unmet treatment of pathologies, including type-II diabetes, cancers, inflammation, and neurodegenerative disease. Recent advances in drug discovery targeting GSK-3 involved extensive computational modeling techniques. Both ligand/structure-based approaches have been well explored to design ATP-competitive inhibitors. Molecular modeling plus dynamics simulations can provide insight into the protein-substrate and protein-protein interactions at substrate binding pocket and C-lobe hydrophobic groove, which will benefit the discovery of non-ATP-competitive inhibitors. To identify structurally novel and diverse compounds that effectively inhibit GSK-3Ăą, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to the induced-fit effects at the ATP-competitive binding pocket of GSK-3Ăą have been explored. An enrichment study was employed to verify the robustness of ensemble docking compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening experiment underwent biological validation. The bioassay results shothat 15 out of the 24 hit compounds are indeed GSK-3Ăą inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization. To further identify structurally novel GSK-3Ăą inhibitors, we performed virtual screening by implementing another mixed ligand-based/structure-based approach, which included quantitative structure-activity relationship (QSAR) analysis and docking prediction. To integrate and analyze complex data sets from multiple experimental sources, we drafted and validated hierarchical QSAR, which adopts a multi-level structure to take data heterogeneity into account. A collection of 728 GSK-3 inhibitors with diverse structural scaffolds were obtained from published papers of 7 research groups based on different experimental protocols. Support vector machines and random forests were implemented with wrapper-based feature selection algorithms in order to construct predictive learning models. The best models for each single group of compounds were then selected, based on both internal and external validation, and used to build the final hierarchical QSAR model. The predictive performance of the hierarchical QSAR model can be demonstrated by an overall R2 of 0.752 for the 141 compounds in the test set. The compounds obtained from the virtual screening experiment underwent biological validation. The bioassay results confirmed that 2 hit compounds are indeed GSK-3Ăą inhibitors exhibiting sub-micromolar inhibitory activity, and therefore validated hierarchical QSAR as an effective approach to be used in virtual screening experiments. We have successfully implemented a variant of supervised learning algorithm, named multiple-instance learning, in order to predict bioactive conformers of a given molecule which are responsible for the observed biological activity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule. The proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers

    On the Use of Imaging Spectroscopy from Unmanned Aerial Systems (UAS) to Model Yield and Assess Growth Stages of a Broadacre Crop

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    Snap bean production was valued at $363 million in 2018. Moreover, the increasing need in food production, caused by the exponential increase in population, makes this crop vitally important to study. Traditionally, harvest time determination and yield prediction are performed by collecting limited number of samples. While this approach could work, it is inaccurate, labor-intensive, and based on a small sample size. The ambiguous nature of this approach furthermore leaves the grower with under-ripe and over-mature plants, decreasing the final net profit and the overall quality of the product. A more cost-effective method would be a site-specific approach that would save time and labor for farmers and growers, while providing them with exact detail to when and where to harvest and how much is to be harvested (while forecasting yield). In this study we used hyperspectral (i.e., point-based and image-based), as well as biophysical data, to identify spectral signatures and biophysical attributes that could schedule harvest and forecast yield prior to harvest. Over the past two decades, there have been immense advances in the field of yield and harvest modeling using remote sensing data. Nevertheless, there still exists a wide gap in the literature covering yield and harvest assessment as a function of time using both ground-based and unmanned aerial systems. There is a need for a study focusing on crop-specific yield and harvest assessment using a rapid, affordable system. We hypothesize that a down-sampled multispectral system, tuned with spectral features identified from hyperspectral data, could address the mentioned gaps. Moreover, we hypothesize that the airborne data will contain noise that could negatively impact the performance and the reliability of the utilized models. Thus, We address these knowledge gaps with three objectives as below: 1. Assess yield prediction of snap bean crop using spectral and biophysical data and identify discriminating spectral features via statistical and machine learning approaches. 2. Evaluate snap bean harvest maturity at both the plant growth stage and pod maturity level, by means of spectral and biophysical indicators, and identify the corresponding discriminating spectral features. 3. Assess the feasibility of using a deep learning architecture for reducing noise in the hyperspectral data. In the light of the mentioned objectives, we carried out a greenhouse study in the winter and spring of 2019, where we studied temporal change in spectra and physical attributes of snap-bean crop, from Huntington cultivar, using a handheld spectrometer in the visible- to shortwave-infrared domain (400-2500 nm). Chapter 3 of this dissertation focuses on yield assessment of the greenhouse study. Findings from this best-case scenario yield study showed that the best time to study yield is approximately 20-25 days prior to harvest that would give out the most accurate yield predictions. The proposed approach was able to explain variability as high as R2 = 0.72, with spectral features residing in absorption regions for chlorophyll, protein, lignin, and nitrogen, among others. The captured data from this study contained minimal noise, even in the detector fall-off regions. Moving the focus to harvest maturity assessment, Chapter 4 presents findings from this objective in the greenhouse environment. Our findings showed that four stages of maturity, namely vegetative growth, budding, flowering, and pod formation, are distinguishable with 79% and 78% accuracy, respectively, via the two introduced vegetation indices, as snap-bean growth index (SGI) and normalized difference snap-bean growth index (NDSI), respectively. Moreover, pod-level maturity classification showed that ready-to-harvest and not-ready-to-harvest pods can be separated with 78% accuracy with identified wavelengths residing in green, red edge, and shortwave-infrared regions. Moreover, Chapters 5 and 6 focus on transitioning the learned concepts from the mentioned greenhouse scenario to UAS domain. We transitioned from a handheld spectrometer in the visible to short-wave infrared domain (400-2500 nm) to a UAS-mounted hyperspectral imager in the visible-to-near-infrared region (400-1000 nm). Two years worth of data, at two different geographical locations, were collected in upstate New York and examined for yield modeling and harvest scheduling objectives. For analysis of the collected data, we introduced a feature selection library in Python, named “Jostar”, to identify the most discriminating wavelengths. The findings from the yield modeling UAS study show that pod weight and seed length, as two different yield indicators, can be explained with R2 as high as 0.93 and 0.98, respectively. Identified wavelengths resided in blue, green, red, and red edge regions, and 44-55 days after planting (DAP) showed to be the optimal time for yield assessment. Chapter 6, on the other hand, evaluates maturity assessment, in terms of pod classification, from the UAS perspective. Results from this study showed that the identified features resided in blue, green, red, and red-edge regions, contributing to F1 score as high as 0.91 for differentiating between ready-to-harvest vs. not ready-to-harvest. The identified features from this study is in line with those detected from the UAS yield assessment study. In order to have a parallel comparison of the greenhouse study against the UAS study, we adopted the methodology employed for UAS studies and applied it to the greenhouse studies, in Chapter 7. Since the greenhouse data were captured in the visible-to-shortwave-infrared (400-2500 nm) domain, and the UAS study data were captured in the VNIR (400-1000 nm) domain, we truncated the spectral range of the collected data from the greenhouse study to the VNIR domain. The comparison experiment between the greenhouse study and the UAS studies for yield assessment, at two harvest stages early and late, showed that spectral features in 450-470, 500-520, 650, 700-730 nm regions were repeated on days with highest coefficient of determination. Moreover, 46-48 DAP with high coefficient of determination for yield prediction were repeated in five out of six data sets (two early stages, each three data sets). On the other hand, the harvest maturity comparison between the greenhouse study and the UAS data sets showed that similar identified wavelengths reside in ∌450, ∌530, ∌715, and ∌760 nm regions, with performance metric (F1 score) of 0.78, 0.84, and 0.9 for greenhouse, 2019 UAS, and 2020 UAS data, respectively. However, the incorporated noise in the captured data from the UAS study, along with the high computational cost of the classical mathematical approach employed for denoising hyperspectral data, have inspired us to leverage the computational performance of hyperspectral denoising by assessing the feasibility of transferring the learned concepts to deep learning models. In Chapter 8, we approached hyperspectral denoising in spectral domain (1D fashion) for two types of noise, integrated noise and non-independent and non-identically distributed (non-i.i.d.) noise. We utilized Memory Networks due to their power in image denoising for hyperspectral denoising, introduced a new loss and benchmarked it against several data sets and models. The proposed model, HypeMemNet, ranked first - up to 40% in terms of signal-to-noise ratio (SNR) for resolving integrated noise, and first or second, by a small margin for resolving non-i.i.d. noise. Our findings showed that a proper receptive field and a suitable number of filters are crucial for denoising integrated noise, while parameter size was shown to be of the highest importance for non-i.i.d. noise. Results from the conducted studies provide a comprehensive understanding encompassing yield modeling, harvest scheduling, and hyperspectral denoising. Our findings bode well for transitioning from an expensive hyperspectral imager to a multispectral imager, tuned with the identified bands, as well as employing a rapid deep learning model for hyperspectral denoising

    Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

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    India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants

    Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

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    Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

    Africa-EU Renewable Energy Research and Innovation Symposium 2018 (RERIS 2018)

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    This open access book presents the proceedings of the 2nd Africa-EU Renewable Energy Research and Innovation Symposium (RERIS 18), held in Maseru, Lesotho in January 2018. The symposium aimed to foster research cooperation on renewable energy between Africa and Europe – in academia, as well as the private and public sectors. Addressing thematic areas such as ‱ Grid-connected renewable energy; ‱ Decentralised renewable and household energy solutions; ‱ Energy socioeconomics; and ‱ Promotion of energy research, innovation, education and entrepreneurship, the book brings together contributions from academics and practitioners from the EU and Africa to enable mutual learning and knowledge transfer – a key factor in boosting sustainable development in the African renewable energy market. It also plays a significant role in promoting African renewable energy research, which helps to secure energy supply in both rural and urban areas and to increase generation capacities and energy system resilience. This book is an invaluable resource for academics and professionals across the renewable energy spectrum
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