Ghost-Attention-YOLOv8: Enhancing Rice Leaf Disease Detection with Lightweight Feature Extraction and Advanced Attention Mechanisms
Year: 2025
Type: article
Abstract: In agricultural research, effective and efficient disease detection in crops is crucial for enhancing yield and sustainability. This study presents a novel approach to improving YOLOv8, a state-of-the-art object detection model, by integrating the Ghost model with three advanced attention mechanisms: Convolutional Block Attention Module (CBAM), Triplet Attention, and Efficiency Multi-Scale Attention (EMA). The Ghost model optimizes feature extraction by reducing computational complexity, while the attention modules enable the model to focus on relevant regions, improving detection performance. The resulting Ghost-Attention-YOLOv8 model was evaluated on the Rice Leaf Disease dataset to assess its efficacy in identifying and classifying various diseases. The experimental results demonstrate significant improvements in accuracy, precision, and recall compared to the baseline YOLOv8 model. The proposed Ghost YOLOv8s with Efficiency Multi-Scale Attention model achieves a parameter count of 5.5 M, a reduction of 4.3 million compared to the original YOLOv8s model, while the accuracy is improved: the mAP@50 metric reaches 95.4%, a 2.3% increase; and mAP@50–95 improves to 62.4%, an increase of 3.7% over the original YOLOv8s. This research offers a practical solution to the challenges of computational efficiency and accuracy in agricultural monitoring, contributing to the development of robust AI tools for disease detection in crops.
Source: AgriEngineering
Authors: Thanh Dang Bui, Tra My Do Le
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4408894720