Advanced deep learning model for crop-specific and cross-crop pest identification

Abstract

The agricultural pests across diverse crops pose significant challenges in identification due to their diminutive size, natural camouflage, and the complex, cluttered environments they inhabit. This paper proposes an advanced deep-learning model to address these issues. The Key components of this customized solution include the “Channel-Enhanced Generalized Efficient Layer Aggregation Network” module, which enhances and highlights the features through the channel and spatial enhancement mechanisms. The “Generalized Multi-Scale Feature Extraction” module employs multi-scale feature extraction to provide fine-grain, inter-scale, and rich pest features. Additionally, a custom re-parameterization technique was adapted to optimize the real-time performance and boost the model’s efficiency. The model’s effectiveness was rigorously evaluated using the proposed Jute17 dataset. Experimental results demonstrate significant performance improvements over the Baseline model, achieving a 9.2% increase in Precision, and the detection speed retains high efficiency on the Jute17 dataset. Furthermore, the benchmark datasets Pest24 and IP102 were added to validate the performance of the proposed model, and it outperformed the Baseline model, Faster RCNN, Deformable-detr, and other YOLO series. The proposed model attained a mean Average Precision (mAP) of 78.22% on the Pest24 dataset and 78.15% accuracy on the IP102 dataset. This method offers a practical and efficient agricultural crop-specific and cross-crop pest management solution for complex field environments.