MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism
Agricultural pest detection is critical for crop protection and food security, yet existing methods suffer from low computational efficiency and poor generalization due to imbalanced data distribution, minimal inter-class variations among pest categories, and significant intra-class differences. To...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/7/1549 |
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| Summary: | Agricultural pest detection is critical for crop protection and food security, yet existing methods suffer from low computational efficiency and poor generalization due to imbalanced data distribution, minimal inter-class variations among pest categories, and significant intra-class differences. To address the high computational complexity and inadequate feature representation in traditional convolutional networks, this study proposes MA-YOLO, an agricultural pest detection model based on multi-scale fusion and attention mechanisms. The SDConv module reduces computational costs through depthwise separable convolution and dynamic group convolution while enhancing local feature extraction. The LDSPF module captures multi-scale information via parallel dilated convolutions with spatial attention mechanisms and dual residual connections. The ASCC module improves feature discriminability by establishing an adaptive triple-weight system for global, channel, and spatial semantic responses. The MDF module balances efficiency and multi-scale feature extraction using multi-branch depthwise separable convolution and soft attention-based dynamic weighting. Experimental results demonstrate detection accuracies of 65.4% and 73.9% on the IP102 and Pest24 datasets, respectively, representing improvements of 2% and 1.8% over the original YOLOv11s network. These results establish MA-YOLO as an effective solution for automated agricultural pest monitoring with applications in precision agriculture and crop protection systems. |
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| ISSN: | 2073-4395 |