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

Author:

Kong Jianlei12ORCID,Xiao Yang1,Jin Xuebo1ORCID,Cai Yuanyuan23ORCID,Ding Chao34,Bai Yuting24ORCID

Affiliation:

1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

2. National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China

3. College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China

4. Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 100048, China

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.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Open project of China Food Flavor and Nutrition Health Innovation Center of Beijing Technology and Business University

Project of Beijing Municipal University Teacher Team Construction Support Plan

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

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