Open AccessTomato Pests and Diseases Classification Model Based on Optimized Convolutional Neural Network
Author(s) -
Shaopeng Jia,
Hewei Gao,
Hongling Xiao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1437/1/012052
Subject(s) - convolutional neural network , regularization (linguistics) , artificial intelligence , computer science , robustness (evolution) , feature extraction , upload , machine learning , deep learning , pattern recognition (psychology) , data mining , biochemistry , chemistry , gene , operating system
In the field of agricultural information processing, automatic identification and diagnosis of common diseases of tomatoes play an important role. The emergence of deep learning can help people simplify the process of image feature extraction, reduce network complexity and improve recognition accuracy. This paper built the Inception-v3 model to identify and classify five classes of tomato pests and diseases. We collected some images of tomato diseases uploaded by farmers from the online diagnosis platform for crop pests and diseases. In the experiment, the combination of the moving average model and the regularization was used to adjust the parameters in the model and enhance the robustness of the model on the test data. Experiments showed that the Inception-v3 model based on regular expression and moving average optimization can effectively avoid regularization and has higher accuracy, which can reach 86.9%.