ASSESSMENT OF PLANT DISEASE DETECTION BY DEEP LEARNING
Alpyssov A. Uzakkyzy N. Talgatbek A. Moldasheva R. Bekmagambetova G. Yessekeyeva M. Kenzhaliev D. Yerzhan A. Tolstoy A.
2023Technology Center
Eastern-European Journal of Enterprise Technologies
2023#1Issue 2-12141 - 48 pp.
Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadvantages of each method. A common data set is introduced and the results of existing studies are compared. This study discusses possible problems in the practical application of plant disease and pest detection based on deep learning. Conventional image processing algorithms or manual descriptive design and classifiers are often used for traditional computer vision-based plant disease and pest detection. This method usually uses various characteristics of plant diseases and pests to create an image layout and selects a useful light source and shooting angle to produce evenly lit images. The purpose of this work is to identify a group of pests and diseases of domestic and garden plants using a mobile application and display the final result on the screen of a mobile device. In this work, data from 38 different classes were used, including diseased and healthy leaf images of 13 plants from plantVillage. In the experiment, Inception v3 tends to consistently improve accuracy with an increasing number of epochs with no sign of overfitting and performance degradation. Keras with Theano backend used to teach architectures
classification , clustering , deep learning , image processing , Inception v3 , plant diseases
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Graduate School of Natural Science, Pavlodar Pedagogical University, Mir str.,60, Pavlodar, 140000, Kazakhstan
Department of Computer and Software Engineering, L. N. Gumilyov Eurasian National University, Satpayev str.,2, Astana, 010008, Kazakhstan
Department of Information and Communication Technologies, International Taraz Innovative Institute, Zheltoksan str.,69B, Taraz, 080000, Kazakhstan
Specialty 8D06101 Big Data Analytics, Department of Information Systems, S. Seifullin Kazakh Agrotechnical University, Zhenis аve.,62, Astana, 010011, Kazakhstan
Department of Information Technologies, Kazakh University of Technology and Business, K. Mukhamedkhanova str.,37A, Astana, 010000, Kazakhstan
Physical and Mathematical Sciences, Department of Information Systems and Technologies, Esil University, Zhubanov str.,7, Astana, 010000, Kazakhstan
Department of General and Theoretical Physics, L. N. Gumilyov Eurasian National University, Satpayev str.,2, Astana, 010008, Kazakhstan
Department of Telecommunications and Innovative Technologies, Almaty University of Power Engineering and Telecommunications named after Gumarbek Daukeyev, Baytursynuli str.,126/1, Almaty, 050013, Kazakhstan
Department of Radio Engineering, Electronics and Telecommunications, L. N. Gumilyov Eurasian National University, Satpayev str.,2, Astana, 010008, Kazakhstan
Graduate School of Natural Science
Department of Computer and Software Engineering
Department of Information and Communication Technologies
Specialty 8D06101 Big Data Analytics
Department of Information Technologies
Physical and Mathematical Sciences
Department of General and Theoretical Physics
Department of Telecommunications and Innovative Technologies
Department of Radio Engineering
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