ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases

Yujia Zhang, Luteng Zhong, Yu Ding (), Hongfeng Yu and Zhaoyu Zhai ()
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Yujia Zhang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Luteng Zhong: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Yu Ding: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Hongfeng Yu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Zhaoyu Zhai: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China

Agriculture, 2023, vol. 13, issue 6, 1-17

Abstract: Rice is a staple food for over half of the global population, but it faces significant yield losses: up to 52% due to leaf blast disease and brown spot diseases, respectively. This study aimed at proposing a hybrid architecture, namely ResViT-Rice, by taking advantage of both CNN and transformer for accurate detection of leaf blast and brown spot diseases. We employed ResNet as the backbone network to establish a detection model and introduced the encoder component from the transformer architecture. The convolutional block attention module was also integrated to ResViT-Rice to further enhance the feature-extraction ability. We processed 1648 training and 104 testing images for two diseases and the healthy class. To verify the effectiveness of the proposed ResViT-Rice, we conducted comparative evaluation with popular deep learning models. The experimental result suggested that ResViT-Rice achieved promising results in the rice disease-detection task, with the highest accuracy reaching 0.9904. The corresponding precision, recall, and F1-score were all over 0.96, with an AUC of up to 0.9987, and the corresponding loss rate was 0.0042. In conclusion, the proposed ResViT-Rice can better extract features of different rice diseases, thereby providing a more accurate and robust classification output.

Keywords: leaf blast disease; brown spot disease; hybrid architecture; transformer encoder; convolutional neural network (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: 2023
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