Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion
Apples are a highly valuable economic crop worldwide, but their cultivation often faces challenges from pests and diseases that severely affect yield and quality. To address this issue, this study proposes an improved pest and disease detection algorithm, YOLO-PEL, based on YOLOv11, which integrates...
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MDPI AG
2025-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1043 |
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| author | Weihao Bao Fuquan Zhang |
| author_facet | Weihao Bao Fuquan Zhang |
| author_sort | Weihao Bao |
| collection | DOAJ |
| description | Apples are a highly valuable economic crop worldwide, but their cultivation often faces challenges from pests and diseases that severely affect yield and quality. To address this issue, this study proposes an improved pest and disease detection algorithm, YOLO-PEL, based on YOLOv11, which integrates multiple innovative modules, including PMFEM, EHFPN, and LKAP, combined with data augmentation strategies, significantly improving detection accuracy and efficiency in complex environments. PMFEM leverages partial multi-scale feature extraction to effectively enhance feature representation, particularly improving the ability to capture pest and disease targets in complex backgrounds. EHFPN employs hierarchical feature fusion and an efficient local attention mechanism to markedly improve the detection accuracy of small targets. LKAP introduces a large kernel attention mechanism, expanding the receptive field and enhancing the localization precision of diseased regions. Experimental results demonstrate that YOLO-PEL achieves a mAP@50 of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>72.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the Turkey_Plant dataset’s apple subset, representing an improvement of approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.3</mn><mo>%</mo></mrow></semantics></math></inline-formula> over the baseline YOLOv11. Furthermore, the model exhibits favorable lightweight characteristics in terms of computational complexity and parameter count, underscoring its effectiveness and robustness in practical applications. YOLO-PEL not only provides an efficient solution for agricultural pest and disease detection, but also offers technological support for the advancement of smart agriculture. Future research will focus on optimizing the model’s speed and lightweight design to adapt to broader agricultural application scenarios, driving further development in agricultural intelligence technologies. |
| format | Article |
| id | doaj-art-60132e5ec65848f097ebe0ea444dea5f |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-60132e5ec65848f097ebe0ea444dea5f2025-08-20T02:33:43ZengMDPI AGAgronomy2073-43952025-04-01155104310.3390/agronomy15051043Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature FusionWeihao Bao0Fuquan Zhang1College of Information Science and Technology, NanJing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology, NanJing Forestry University, Nanjing 210037, ChinaApples are a highly valuable economic crop worldwide, but their cultivation often faces challenges from pests and diseases that severely affect yield and quality. To address this issue, this study proposes an improved pest and disease detection algorithm, YOLO-PEL, based on YOLOv11, which integrates multiple innovative modules, including PMFEM, EHFPN, and LKAP, combined with data augmentation strategies, significantly improving detection accuracy and efficiency in complex environments. PMFEM leverages partial multi-scale feature extraction to effectively enhance feature representation, particularly improving the ability to capture pest and disease targets in complex backgrounds. EHFPN employs hierarchical feature fusion and an efficient local attention mechanism to markedly improve the detection accuracy of small targets. LKAP introduces a large kernel attention mechanism, expanding the receptive field and enhancing the localization precision of diseased regions. Experimental results demonstrate that YOLO-PEL achieves a mAP@50 of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>72.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the Turkey_Plant dataset’s apple subset, representing an improvement of approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.3</mn><mo>%</mo></mrow></semantics></math></inline-formula> over the baseline YOLOv11. Furthermore, the model exhibits favorable lightweight characteristics in terms of computational complexity and parameter count, underscoring its effectiveness and robustness in practical applications. YOLO-PEL not only provides an efficient solution for agricultural pest and disease detection, but also offers technological support for the advancement of smart agriculture. Future research will focus on optimizing the model’s speed and lightweight design to adapt to broader agricultural application scenarios, driving further development in agricultural intelligence technologies.https://www.mdpi.com/2073-4395/15/5/1043deep learningimage processingapple pest and disease detectionfeature extractionmulti-scale detection |
| spellingShingle | Weihao Bao Fuquan Zhang Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion Agronomy deep learning image processing apple pest and disease detection feature extraction multi-scale detection |
| title | Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion |
| title_full | Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion |
| title_fullStr | Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion |
| title_full_unstemmed | Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion |
| title_short | Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion |
| title_sort | apple pest and disease detection network with partial multi scale feature extraction and efficient hierarchical feature fusion |
| topic | deep learning image processing apple pest and disease detection feature extraction multi-scale detection |
| url | https://www.mdpi.com/2073-4395/15/5/1043 |
| work_keys_str_mv | AT weihaobao applepestanddiseasedetectionnetworkwithpartialmultiscalefeatureextractionandefficienthierarchicalfeaturefusion AT fuquanzhang applepestanddiseasedetectionnetworkwithpartialmultiscalefeatureextractionandefficienthierarchicalfeaturefusion |