Abstract
Durian is a major tropical crop in Southeast Asia, but its yield and quality are severely impacted by a range of pests and diseases. Manual inspection remains the dominant detection method but suffers from high labor intensity, low accuracy, and difficulty in scaling. To address these challenges, this paper proposes EN-YOLO, a novel enhanced YOLO-based deep learning model that integrates the EfficientNet backbone and multimodal attention mechanisms for precise detection of durian pests and diseases. The model removes redundant feature layers and introduces a large-span residual edge to preserve key spatial information. Furthermore, a multimodal input strategy-incorporating RGB, near-infrared and thermal imaging-is used to enhance robustness under variable lighting and occlusion. Experimental results on real orchard datasets demonstrate that EN-YOLO outperforms YOLOv8 (You Only Look Once version 8), YOLOv5-EB (You Only Look Once version 5-Efficient Backbone), and Fieldsentinel-YOLO in detection accuracy, generalization, and small-object recognition. It achieves a 95.3% counting accuracy and shows superior performance in ablation and cross-scene tests. The proposed system also supports real-time drone deployment and integrates an expert knowledge base for intelligent decision support. This work provides an efficient, interpretable, and scalable solution for automated pest and disease management in smart agriculture.