Assessment of plant disease detection by deep learning
DOI:
https://doi.org/10.15587/1729-4061.2023.274483Keywords:
image processing, Inception v3, deep learning, classification, plant diseases, clusteringAbstract
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
Supporting Agency
- For providing data on agricultural crops of Northern Kazakhstan in the preparation of this article, the author expresses gratitude to the Scientific and Production Center of Grain Farming named after A. I. Barayev.
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Copyright (c) 2023 Akan Alpyssov, Nurgul Uzakkyzy, Ayazbaev Talgatbek, Raushan Moldasheva, Gulmira Bekmagambetova, Mnyaura Yessekeyeva, Dossym Kenzhaliev, Assel Yerzhan, Ailanysh Tolstoy
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