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Optimization of Deep Learning Model for plant Disease detection Using Particle Swarm Optimizer

Computers, Materials & Continua 2022年第5期71卷 4019-4031页

作者： Ahmed Elaraby Walid Hamdy Madallah Alruwaili Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Department of Math. &Computer Science Faculty of SciencePort Said UniversityEgypt College of Computer and Information Sciences Jouf UniversitySakakaAljoufKingdom of Saudi Arabia

plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still *** losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our *** of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error *** learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and *** proposed work describes a deep learning approach for detection plant ***,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf *** our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic *** our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.

关键词： Deep neural networks plant diseases detection classification AlexNet PSO

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plant Disease Diagnosis and Image Classification Using Deep Learning

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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 with Deep Learning and Feature Extraction Using plant Village

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Journal of Computer and Communications 2020年第6期8卷 10-22页

作者： Faye Mohameth Chen Bingcai Kane Amath Sada School of Computer Science & Technology Dalian University of Technology Dalian China School of Computer Science & Technology Xinjiang Normal University Urumqi China

Nowadays, crop diseases are a crucial problem to the world’s food supplies, in a world where the population count is around 7 billion people, with more than 90% not getting access to the use of tools or features that would identify and solve the problem. At present, we live in a world dominated by technology on a significant scale, major network coverage, high-end smart-phones, as long as there are great discoveries and improvements in AI. The combination of high-end smart-phones and computer vision via Deep Learning has made possible what can be defined as “smartphone-assisted disease diagnosis”. In the area of Deep Learning, multiple architecture models have been trained, some achieving performance reaching more than 99.53% [1]. In this study, we evaluate CNN’s architectures applying transfer learning and deep feature extraction. All the features obtained will also be classified by SVM and KNN. Our work is feasible by the use of the open source plant Village Dataset. The result obtained shows that SVM is the best classifier for leaf’s diseases detection.

关键词： plant diseases detection Feature Extraction Transfer Learning SVM KNN

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