Rapid detection of common scab, powdery scab, and enlarged lenticels in potato tubers using deep learning
The YOLOv8-ST model enables fast, accurate, and reliable detection of common scab, powdery scab, and enlarged lenticels on potato tubers. This field-deployable solution supports early disease diagnosis and timely intervention, thus reducing crop losses. The model is available through the mobile app Plant Guardian, enabling growers to identify potato skin blemishes directly in the field, thereby advancing both practical disease management and agricultural AI applications. © 2025 Society of Chemical Industry.
Authors
- Jialin Yu (ORCID: https://orcid.org/0000-0003-0287-5081)
- Utpal Handique (ORCID: https://orcid.org/0000-0002-8783-5814)
- Jiale Lv
- Jian Wu (ORCID: https://orcid.org/0000-0001-8332-9861)
- Teng Liu (ORCID: https://orcid.org/0000-0002-6715-3998)
- Ruofang Zhang
- Wenpeng Zhu (ORCID: https://orcid.org/0009-0006-1091-1459)
Institutions
- Inner Mongolia Agricultural University (CN)
- Peking University (CN)
- Inner Mongolia University (CN)
- Xinjiang Uygur Autonomous Region Product Quality Supervision and Inspection Institute (CN)
- Inner Mongolia Electric Power Survey & Design Institute (China) (CN)
- Inner Mongolia Autonomous Region Meteorological Bureau (CN)
- Weifang University (CN)
Publication Details
- Journal
- Pest Management Science
- Published
- 2025-12-05
- DOI
- https://doi.org/10.1002/ps.70424
- Primary Topic
- Plant Disease Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
- Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region