Image recognition of four rice leaf diseases based on deep learning and support vector machine

By F. Jiang, Yu Chen, Di Cai, Gongfa Li. Computers and Electronics in Agriculture, 2020

352 citations recorded by the source corpus.

Abstract In the field of agricultural information, identification and prediction of rice leaf diseases has always been a research focus. Deep learning and support vector machine (SVM) technology are hot research topics in the field of pattern recognition at present. Their combination can not only solve the problem effectively, but also improve the recognition accuracy. In this study, firstly, we use convolution neural networks (CNNs) to extract the rice leaf disease images features. Then the SVM method is applied to classify and predict the specific disease. The optimal parameters of SVM model are obtained through the 10-fold cross validation method. The experimental results show that when the penalty parameter C = 1 and the kernel parameter g = 50, the average correct recognition rate of the rice disease recognition model based on deep learning and SVM is 96.8%. This accuracy is higher than that of the traditional back propagation neural networks models. This study provides a new method for the further research of crop diseases diagnosis by using deep learning.

Subfields: Analytical Chemistry, Atmospheric Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/918aea5a90374718321741d0de04af1cecf8b429

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Shujuan Zhang
2. Lili Li
3. Bin Wang
4. Wubetu Barud Demilie

In Plant Science

1. Jinzhu Lu
2. Shujuan Zhang
3. Li Tan
4. Lili Li
5. Bin Wang
6. Huanyu Jiang
7. Wubetu Barud Demilie

Citations in the corpus

• Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification (2021, background): “Rarely used [24,25]: They often need to manually design and extract features, which require research staff to possess professional capabilities.”
• Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “Jiang et al. [56] used the Mean Shift algorithm to segment four kinds of rice disease spot (red blight stripe disease to rice blast and sheath blight) at first, and then extract shape feature by artificial calculation (put forward three new shape characteristic lesions number N, S lesion area, number…”