ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1620339
This article is part of the Research TopicPlant Pest and Disease Model Forecasting: Enhancing Precise and Data-Driven Agricultural PracticesView all 14 articles
GhostConv+CA-YOLOv8n: A lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds
Provisionally accepted
- Heilongjiang Bayi Agricultural University, daqing, China
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Deep learning models for rice pest detection often face performance degradation in real-world field environments due to complex backgrounds and limited computational resources. Existing approaches suffer from two critical limitations: (1) inadequate feature representation under occlusion and scale variations, and (2) excessive computational costs for edge deployment.The GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed. Firstly, GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n's backbone structure, reducing parameters by 40,458 while maintaining feature richness. Then, the Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n's neck structure. This module enhance low-level feature representation by fusing global and local context, which is particularly effective for detecting occluded pests in complex environments. Finally, Shape-IoU, which improves bounding box regression by accounting for target morphology, and Slide Loss, which addresses class imbalance by dynamically adjusting sample weighting during training were employed.Comprehensive evaluations on the Ricepest15 dataset, GhostConv+CA-YOLOv8n achieves 89.959% precision and 82.258% recall with improvements of 3.657% and 11.59%, and the model parameter reduced 1.34%, over the YOLOv8n baseline while maintaining a high mAP (94.527% vs. 84.994% baseline). Furthermore, the model shows strong generalization, achieving a 4.49%, 5.452%, and 3.407% improvement in F1-score, precision, and recall on the IP102 benchmark.This study bridges the gap between accuracy and efficiency for in field pest detection, providing a practical solution for real-time rice monitoring in smart agriculture systems.
Keywords: Rice pest detection, GhostConv, Context Aggregation Block, Shape-IoU, Slide Loss ch_in, C2:ch_out, Imagesize:W*H Kernel:1, Stride:1
Received: 29 Apr 2025; Accepted: 17 Jun 2025.
Copyright: © 2025 Li, Lu, Ma, Yin and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Lu, Heilongjiang Bayi Agricultural University, daqing, China
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