Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important studyarea is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plantillnesses. The process of choosing a classification method is always challenging because the quality of the results can differ depending on the input data. K-Nearest Neighbor Classifier (KNN), Probabilistic NeuralNetwork (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis,Artificial Neural Network (ANN), and Fuzzy Logic are a few examples of diverse classification algorithms.Classifications of plant leaf diseases have many uses in a variety of industries, including agriculture andbiological research. Presymptomatic diagnosis and crop health information can aid in the ability to managepathogens through proper management approaches. Convolutional neural networks (CNNs) are the mostwidely used DL models for computer vision issues since they have proven to be very effective in tasks likepicture categorization, object detection, image segmentation, etc. The experimental findings demonstrate theproposed model's superior performance to pre-trained models such as VGG16 and InceptionV3. The range ofcategorization accuracy is 76% to 100%, based on

Artificial Intelligence Decision Support System Based on Artificial Neural Networks to Predict the Commercialization Time by the Evolution of Peach Quality

Climacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical‐chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support Sys‐ tem (DSS) designed to predict the evolution of the quality of peaches, namely the storage time re‐ quired before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and val‐ idation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple predic‐ tion of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and con‐ sumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI‐based solutions for smart cities.This study is within the activities of project PrunusPós—Otimização de processos de ar‐ mazenamento, conservação em frio, embalamento ativo e/ou inteligente, e rastreabilidade da qual‐ idade alimentar no póscolheita de produtos frutícolas (Optimization of processes of storage, cold conservation, active and/or intelligent packaging, and traceability of food quality in the postharvest of fruit products), Operation n.º PDR2020‐101‐031695 (Partner), Consortium n.º 87, Initiative n.º 175 promoted by PDR2020 and co‐financed by FEADER under the Portugal 2020 initiative.info:eu-repo/semantics/publishedVersio

A deep learning-based mobile app system for visual identification of tomato plant disease

Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%

REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES

The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning

Estudo e análise de Redes Neurais Convolucionais Profundas na identificação de doenças em plantas por imagens

Tese (doutorado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2022.Rede Neurais Convolucionais (CNNs), demonstram um potencial para tarefas relacionadas à Visão Computacional. A característica de maior destaque das CNNs é sua capacidade de explorar a correlação espacial ou temporal nos dados. Assim, várias melhorias na metodologia e arquitetura de aprendizagem das redes foram realizadas para tornar as CNNs escaláveis para problemas grandes, heterogêneos, complexos e multiclasses. A agricultura delimita um escopo de problemas desafiadores, que carecem de tecnologias para proporcionar maior incremento na produção agrícola, principalmente em relação ao enfrentamento de doenças. As doenças de plantas são consideradas um dos principais fatores que influenciam a produção de alimentos, e a sua identificação é primordialmente realizada por técnicas manuais ou por microscopia, oque aumenta o tempo de diagnóstico e as possibilidades de erro. Soluções automatizadas de identificação de doenças de plantas, usando imagens e aprendizado de máquina, em especial as CNNs, têm proporcionado avanços significativos. Entretanto, a maioria das abordagens possui baixa capacidade de classificação, tendo como agravante as infestações simultâneas por diferentes patógenos e as confusões sintomáticas causadas por fatores abióticos. Assim, o objetivo deste trabalho é analisar e avaliar as arquiteturas CNNs, explorando potencialidades e prospectando novos arranjos de arquitetura para classificar doenças de plantas e identificar patógenos. A abordagem fez uso de uma estratégia de customização, na qual redes operativas independentes ou blocos convolucionais são integradas em um único modelo para capturar um conjunto mais variado de características. A NEMANeté um resultado relevante desta abordagem de customização de CNNs para classificação de fitonematoides em imagens microscópicas. O mo-delo alcançou a melhor taxa de acurácia atingindo 99,35%, possibilitando melhorias gerais de precisão superiores a 6,83% e 4,1%, para treinamento com inicialização dos pesos e para transferência de aprendizagem, em comparação com outras arquiteturas avaliadas. Os resultados demonstraram que a customização de arquiteturas CNNs é uma abordagem promissora para o aumento de ganhos em termo de acurácia, convergência das redes e tamanho dos modelos.Convolutional Neural Networks (CNNs) demonstrate a potential for computer vision tasks.The most prominent feature of CNNs is their ability to explore spatial or temporal correlationin the data. Thus, several improvements in the methodology and architecture of learning of thenetworks were made to make the CNNs scalable for large, heterogeneous, complex, and multi-class problems. Agriculture delimits a scope of challenging problems, which lack technologiesto increase agricultural production, especially about coping with diseases. Plant diseases areconsidered one of the main factors that influence food production, and their identification is pri-marily performed by manual techniques or microscopy, which increases the time of diagnosisand the possibility of errors. Using imaging and machine learning, especially CNNs, automatedplant disease identification solutions have provided significant advances. However, most appro-aches have low classification capacity, with simultaneous infestations by different pathogensand symptomatic confusion caused by abiotic factors as an aggravating factor. Thus, this workaims to analyze and evaluate CNN architectures, exploring potentialities and prospecting newarchitectural arrangements to classify plant diseases and identify pathogens. The approach useda customization strategy, in which independent operative networks or convolutional blocks areintegrated into a single model to capture a more varied set of characteristics. TheNEMANetis arelevant result of this CNN customization approach for the classification of phytonematodes inmicroscopic images. The model achieved the best accuracy rate reaching 99.35%, enabling ove-rall accuracy improvements greater than 6.83% and 4.1%, for weight initialization training andlearning transfer, compared to other evaluated architectures. The results showed that the custo-mization of CNN architectures is a promising approach to increase gains in terms of accuracy,the convergence of networks, and the size of the model

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

Bowdoin Orient v.139, no.1-26 (2009-2010)

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