A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

Jianlei Kong, Hongxing Wang, Chengcai Yang, Xuebo Jin, Min Zuo and Xin Zhang
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Jianlei Kong: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Hongxing Wang: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Chengcai Yang: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Xuebo Jin: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Min Zuo: National Engineering Laboratory for Agri-Product Quality Traceability, Beijing 100048, China
Xin Zhang: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Agriculture, 2022, vol. 12, issue 4, 1-30

Abstract: With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model’s performance and parameters, which is suitable for its practical deployment in precision agriculture art applications.

Keywords: precision agriculture; crop pests and diseases; fine-grained visual classification; feature-enhanced attention mechanism; higher-order pooling module (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 (3)

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