A Lightweight Neural Network-Based Method for Identifying Early-Blight and Late-Blight Leaves of Potato

Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf imag...

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Published inApplied sciences Vol. 13; no. 3; p. 1487
Main Authors Kang, Feilong, Li, Jia, Wang, Chunguang, Wang, Fuxiang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2023
Subjects
Agricultural production
Agriculture
Artificial intelligence
Classification
convolutional neural networks
Crop diseases
Crops
Deep learning
Django framework
machine learning
Neural networks
Pesticides
potato disease leaf
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Summary:Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf images including early blight, late blight, and healthy. A lightweight, neural network-based model for the identification of early potato leaf diseases significantly reduces the number of model parameters, whereas the accuracy of Top-1 identification is over 93%. We imported the trained model into the Django framework to build a website for a potato early leaf disease identification system, thus providing technical support for the implementation of a mobile-based potato leaf disease identification and early warning system.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13031487