Enhancing Bee Mite Detection with YOLO: The Role of Data Augmentation and Stratified Sampling

Hong-Gu Lee, Jeong-Yong Shin, Su-Bae Kim, Min-Jee Kim, Moon S. Kim, Hoyoung Lee and Changyeun Mo ()
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Hong-Gu Lee: Department of Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon-si 24341, Republic of Korea
Jeong-Yong Shin: Department of Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon-si 24341, Republic of Korea
Su-Bae Kim: Department of Agricultural Biology, National Institute of Agricultural Sciences, Wanju 55365, Republic of Korea
Min-Jee Kim: Department of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Republic of Korea
Moon S. Kim: Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
Hoyoung Lee: Department of Mechatronics Engineering, Korea Polytechnics, 56 Munemi-Ro 448 Beon-Gil, Bupyeong-gu, Incheon 21417, Republic of Korea
Changyeun Mo: Department of Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon-si 24341, Republic of Korea

Agriculture, 2025, vol. 15, issue 11, 1-21

Abstract: Beekeeping is facing a serious crisis due to climate change and diseases such as bee mites ( Varroa destructor ), which have led to declining populations, collapsing colonies, and reduced beekeeping productivity. Bee mites are small, reddish-brown in color, and difficult to distinguish from bees. Rapid bee mite detection techniques are essential for overcoming this crisis. This study developed a technology for recognizing bee mites and beekeeping objects in beecombs using the You Only Look Once (YOLO) object detection algorithm. The dataset was constructed by acquiring RGB images of beecombs containing mites. Regions of interest with a size of 640 × 640 pixels centered on the bee mites were extracted and labeled as seven classes: bee mites, bees, mite-infected bees, larvae, abnormal larvae, and cells. Image processing, data augmentation, and stratified data distribution methods were applied to enhance the object recognition performance. Four datasets were constructed using different augmentation and distribution strategies, including random and stratified sampling. The datasets were partitioned into training, testing, and validation sets in a 7:2:1 ratio, respectively. A YOLO-based model for the detection of bee mites and seven beekeeping-related objects was developed for each dataset. The F1 scores for the detection of bee mites and seven beekeeping-related objectives using the YOLO model based on original datasets were 94.1% and 91.9%, respectively. The model applied data augmentation, and stratified sampling achieved the highest performance, with F1 scores of 97.4% and 96.4% for the detection of bee mites and seven beekeeping-related objects, respectively. The results underscore the efficacy of using the YOLO architecture on RGB images of beecombs for simultaneously detecting bee mites and various beekeeping-related objects. This advanced mite detection method is expected to contribute significantly to the early identification of pests and disease outbreaks, offering a valuable tool for enhancing beekeeping practices.

Keywords: bee mite detection; YOLO object detection algorithm; data augmentation; stratified sampling; beekeeping imaging system (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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