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ASSESSMENT OF PLANT DISEASE DETECTION BY DEEP LEARNING

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dc.contributor.author Alpyssov, A.
dc.contributor.author Uzakkyzy, N.
dc.contributor.author Talgatbek, A.,
dc.contributor.author Moldasheva, R.
dc.contributor.author Bekmagambetova, G.
dc.contributor.author Yessekeyeva, M.,
dc.contributor.author Kenzhaliev, D.
dc.contributor.author Yerzhan, A.
dc.contributor.author Tolstoy, A.
dc.date.accessioned 2024-10-10T09:38:08Z
dc.date.available 2024-10-10T09:38:08Z
dc.date.issued 2023
dc.identifier.citation Alpyssov, A., Uzakkyzy, N., Talgatbek, A., Moldasheva, R., Bekmagambetova, G., Yessekeyeva, M., Kenzhaliev, D., Yerzhan, A., Tolstoy, A. (2023). Assessment of plant disease detection by deep learning. Eastern-European Journal of Enterprise Technologies, 1 (2 (121)), 41–48. doi: https://doi.org/10.15587/1729-4061.2023.274483 ru
dc.identifier.issn 1729-3774
dc.identifier.other doi.org/10.15587/1729-4061.2023.274483
dc.identifier.uri http://rep.enu.kz/handle/enu/17621
dc.description.abstract 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 ru
dc.language.iso en ru
dc.publisher Eastern-European Journal of Enterprise Technologies ru
dc.subject image processing ru
dc.subject Inception v3 ru
dc.subject deep learning ru
dc.subject classification ru
dc.subject plant diseases ru
dc.subject clustering ru
dc.title ASSESSMENT OF PLANT DISEASE DETECTION BY DEEP LEARNING ru
dc.type Article ru


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