Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network

Weidong Zhu, Jun Sun, Simin Wang, Jifeng Shen, Kaifeng Yang and Xin Zhou
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Weidong Zhu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Jun Sun: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Simin Wang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Jifeng Shen: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Kaifeng Yang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Xin Zhou: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2022, vol. 12, issue 8, 1-19

Abstract: The yield and security of grain are seriously infringed on by crop diseases, which are the critical factor hindering the green and high-quality development of agriculture. The existing crop disease identification models make it difficult to focus on the disease spot area. Additionally, crops with similar disease characteristics are easily misidentified. To address the above problems, this paper proposed an accurate and efficient disease identification model, which not only incorporated local and global features of images for feature analysis, but also improved the separability between similar diseases. First, Transformer Encoder was introduced into the improved model as a convolution operation, so as to establish the dependency between long-distance features and extract the global features of the disease images. Then, Centerloss was introduced as a penalty term to optimize the common cross-entropy loss, so as to expand the inter-class difference of crop disease characteristics and narrow their intra-class gap. Finally, according to the characteristics of the datasets, a more appropriate evaluation index was used to carry out experiments on different datasets. The identification accuracy of 99.62% was obtained on Plant Village, and the balanced accuracy of 96.58% was obtained on Dataset1 with a complex background. It showed good generalization ability when facing disease images from different sources. The improved model also balanced the contradiction between identification accuracy and parameter quantity. Compared with pure CNN and Transformer models, the leaf disease identification model proposed in this paper not only focuses more on the disease regions of leaves, but also better distinguishes different diseases with similar characteristics.

Keywords: crop diseases; Transformer Encoder; global features; complex backgrounds; balanced accuracy (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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