LCA-Net: A Lightweight Cross-Stage Aggregated Neural Network for Fine-Grained Recognition of Crop Pests and Diseases

Jianlei Kong, Yang Xiao, Xuebo Jin (), Yuanyuan Cai, Chao Ding and Yuting Bai ()
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Jianlei Kong: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Yang Xiao: 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
Yuanyuan Cai: National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
Chao Ding: College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
Yuting Bai: National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China

Agriculture, 2023, vol. 13, issue 11, 1-23

Abstract: In the realm of smart agriculture technology’s rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a crucial role in ensuring food security, advancing agricultural production, and maintaining food reserves. Nevertheless, existing recognition models encounter inherent limitations such as suboptimal accuracy and excessive computational efforts when dealing with similar pests and diseases in real agricultural scenarios. Consequently, this research introduces the lightweight cross-layer aggregation neural network (LCA-Net). To address the intricate challenge of fine-grained pest identification in agricultural environments, our approach initially enhances the high-performance large-scale network through lightweight adaptation, concurrently incorporating a channel space attention mechanism. This enhancement culminates in the development of a cross-layer feature aggregation (CFA) module, meticulously engineered for seamless mobile deployment while upholding performance integrity. Furthermore, we devised the Cut-Max module, which optimizes the accuracy of crop pest and disease recognition via maximum response region pruning. Thorough experimentation on comprehensive pests and disease datasets substantiated the exceptional fine-grained performance of LCA-Net, achieving an impressive accuracy rate of 83.8%. Additional ablation experiments validated the proposed approach, showcasing a harmonious balance between performance and model parameters, rendering it suitable for practical applications in smart agricultural supervision.

Keywords: smart agricultural management; crop pest and disease; fine-grained image identification; lightweight deep learning; cross-stage aggregation fusion (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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