Plants Disease Image Classification Based on Lightweight Convolution Neural Networks

被引:0
|
作者
Liao, Lili [1 ]
Li, Bo [2 ]
Tang, Jinhong [3 ]
机构
[1] Suzhou Vocat Univ, Sch Comp Engn, Suzhou 215123, Jiangsu, Peoples R China
[2] Shanghai DianJi Univ, Sch Elect Informat Engn, Shanghai 201306, Peoples R China
[3] East China Jiaotong Univ, Sch Informat Engn, Nanchang 330013, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Lightweight convolution neural network; plants diseases; image classification; deep learning;
D O I
10.1142/S0218001422540131
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Plants diseases is a major threat to agricultural production. Reduced yield due to plant diseases can lead to immeasurable economic losses. Therefore, the detection and classification of plant diseases are of great significance. Most of the existing plant disease detection methods focus on improving the identification accuracy. However, besides accuracy, real-time performance cannot be ignored. In this paper, a new module named 2-way residual dense layer is presented to effectively decrease the number of parameters in our network. In this module, depth separable convolution is introduced, which reduces the amount of parameter calculation and achieves a performance of over 98%. Our network is verified by an open dataset which includes 4503 images from four classes, including Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar. The leaf images of these plants have healthy and diseased condition. The experimental results showed that this method can be practically applied to the identification of plant leaf diseases and provide a basis for the identification of other leaf diseases.
引用
收藏
页数:20
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