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Precision agriculture with YOLO-Leaf: advanced methods for detecting apple leaf diseases
Open accessAbstract
The detection of apple leaf diseases plays a crucial role in ensuring crop health and yield. However, due to variations in lighting and shadow, as well as the complex relationships between perceptual fields and target scales, current detection methods face significant challenges. To address these issues, we propose a new model called YOLO-Leaf. Specifically, YOLO-Leaf utilizes Dynamic Snake Convolution (DSConv) for robust feature extraction, employs BiFormer to enhance the attention mechanism, and introduces IF-CIoU to improve bounding box regression for increased detection accuracy and generalization ability. Experimental results on the FGVC7 and FGVC8 datasets show that YOLO-Leaf significantly outperforms existing models in terms of detection accuracy, achieving mAP50 scores of 93.88% and 95.69%, respectively. This advancement not only validates the effectiveness of our approach but also highlights its practical application potential in agricultural disease detection.
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“In autonomous agriculture, YOLO plays a pivotal role in precision farming by identifying pests, diseases, and nutrient deficiencies, thereby enhancing crop yields and optimizing resource efficiency [47].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [47]
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Cites
YOLOv10: Real-Time End-to-End Object Detection
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-3429 · Cited by 3,001
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Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
Metadata
- Authors
- Li, T., Zhang, L., & Lin, J.
- Affiliations
- Lanzhou University of Finance and Economics (CN)
- Nanjing University of Science and Technology (CN)
- Corresponding author
- Zhang, Liyuan
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2024-10-15
- Volume
- 15
- Pages
- 1452502-1452502
- ISSN
- 1664-462X (ISSN-L)
- PMC
- PMC11518753
- OpenAlex
- W4403440761
- Cited by
- 47
- References
- 1
- Citations by year
- 2026: 22 · 2025: 22 · 2024: 3
- FWCI
- 11.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Plant Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62341118)
- Government of Jiangsu Province
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- apple leaf disease detection, object detection, dynamic snake convolution, BiFormer, attention mechanism, bounding box regression, precision agriculture, plant disease diagnosis
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- Li, T., Zhang, L., & Lin, J. (2024). Precision agriculture with YOLO-Leaf: advanced methods for detecting apple leaf diseases. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2024.1452502
