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

被引:11
|
作者
Kang, Feilong [1 ]
Li, Jia [1 ,2 ]
Wang, Chunguang [1 ]
Wang, Fuxiang [1 ]
机构
[1] Inner Mongolia Agr Univ, Hohhot 010018, Peoples R China
[2] Inner Mongolia Autonomous Reg Key Lab Big Data Res, Hohhot 010018, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 03期
基金
中国国家自然科学基金;
关键词
convolutional neural networks; machine learning; potato disease leaf; Django framework;
D O I
10.3390/app13031487
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:10
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