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Plant Disease Diagnosis and Image Classification Using deep learning

Computers, Materials & Continua 2022年第5期71卷 2125-2140页

作者： Rahul Sharma Amar Singh Kavita N.Z.Jhanjhi Mehedi Masud Emad Sami Jaha Sahil Verma School of Computer Applications Lovely Professional University Jalandhar144411India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computer and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the *** farming employs technology to improve *** and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop *** disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s *** learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant *** this paper,the CNN model is proposed for the classification of rice and potato plant leaf *** leaves are diagnosed with bacterial blight,blast,brown spot and tungro *** leaf images are classified into three classes:healthy leaves,early blight and late blight *** leaf dataset with 5932 images and 1500 potato leaf images are used in the *** proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%*** results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.

关键词： Plant diseases detection CNN image classification deep learning in agriculture

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Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

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Artificial Intelligence in agriculture 2021年第1期5卷 90-101页

作者： Punam Bedi Pushkar Gole Department of Computer Science University of DelhiDelhiIndia

Plants are susceptive to various diseases in their growing *** detection of diseases in plants is one of the most challenging problems in *** the diseases are not identified in the early stages,then theymay adversely affect the total yield,resulting in a decrease in the farmers'*** overcome this problem,many researchers have presented different state-of-the-art systems based on deep learning and Machine learning ***,most of these systems either use millions of training parameters or have lowclassification *** paper proposes a novel hybrid model based on Convolutional Autoencoder(CAE)network and Convolutional Neural Network(CNN)for automatic plant disease *** the best of our knowledge,a hybrid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-ofthe-art systems present in the *** this work,the proposed hybrid model is applied to detect Bacterial Spot disease present in peach plants using their leaf images,however,it can be used for any plant disease *** experiments performed in this paper use a publicly available dataset named PlantVillage to get the leaf images of peach *** proposed system achieves 99.35%training accuracy and 98.38%testing accuracy using only 9,914 training *** proposed hybrid model requires lesser number of training parameters as compared to other approaches existing in the ***,in turn,significantly decreases the time required to train the model for automatic plant disease detection and the time required to identify the disease in plants using the trained model.

关键词： Plant disease detection Convolutional autoencoder Convolutional neural network deep learning in agriculture

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