Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast

Yuan Qi, Tan Liu (), Songlin Guo, Peiyan Wu, Jun Ma, Qingyun Yuan, Weixiang Yao and Tongyu Xu
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Yuan Qi: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Tan Liu: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Songlin Guo: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Peiyan Wu: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Jun Ma: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Qingyun Yuan: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Weixiang Yao: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Tongyu Xu: College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China

Agriculture, 2025, vol. 15, issue 15, 1-21

Abstract: Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases.

Keywords: rice blast; hyperspectral; deep learning; feature fusion; severity classification (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: 2025
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