Method for Classifying Apple Leaf Diseases Based on Dual Attention and Multi-Scale Feature Extraction

Jie Ding, Cheng Zhang, Xi Cheng, Yi Yue, Guohua Fan, Yunzhi Wu () and Youhua Zhang
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Jie Ding: Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
Cheng Zhang: Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
Xi Cheng: School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Yi Yue: Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
Guohua Fan: Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
Yunzhi Wu: Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
Youhua Zhang: Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China

Agriculture, 2023, vol. 13, issue 5, 1-19

Abstract: Image datasets acquired from orchards are commonly characterized by intricate backgrounds and an imbalanced distribution of disease categories, resulting in suboptimal recognition outcomes when attempting to identify apple leaf diseases. In this regard, we propose a novel apple leaf disease recognition model, named RFCA ResNet, equipped with a dual attention mechanism and multi-scale feature extraction capacity, to more effectively tackle these issues. The dual attention mechanism incorporated into RFCA ResNet is a potent tool for mitigating the detrimental effects of complex backdrops on recognition outcomes. Additionally, by utilizing the class balance technique in conjunction with focal loss, the adverse effects of an unbalanced dataset on classification accuracy can be effectively minimized. The RFB module enables us to expand the receptive field and achieve multi-scale feature extraction, both of which are critical for the superior performance of RFCA ResNet. Experimental results demonstrate that RFCA ResNet significantly outperforms the standard CNN network model, exhibiting marked improvements of 89.61%, 56.66%, 72.76%, and 58.77% in terms of accuracy rate, precision rate, recall rate, and F1 score, respectively. It is better than other approaches, performs well in generalization, and has some theoretical relevance and practical value.

Keywords: dual attention mechanism; multi-scale feature extraction; RFCA ResNet; 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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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