Identifying an Image-Processing Method for Detection of Bee Mite in Honey Bee Based on Keypoint Analysis

Hong Gu Lee, Min-Jee Kim, Su-bae Kim, Sujin Lee, Hoyoung Lee, Jeong Yong Sin and Changyeun Mo ()
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Hong Gu Lee: Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea
Min-Jee Kim: Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon 24341, Republic of Korea
Su-bae Kim: Apiculture Division, National Institute of Agricultural Science, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Republic of Korea
Sujin Lee: Apiculture Division, National Institute of Agricultural Science, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Republic of Korea
Hoyoung Lee: Department of Mechatronics Engineering, Korea Polytechnics, 56 Munemi-ro 448 beon-gil, Bupyeong-gu, Incheon 21417, Republic of Korea
Jeong Yong Sin: Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
Changyeun Mo: Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea

Agriculture, 2023, vol. 13, issue 8, 1-17

Abstract: Economic and ecosystem issues associated with beekeeping may stem from bee mites rather than other bee diseases. The honey mites that stick to bees are small and possess a reddish-brown color, rendering it difficult to distinguish them with the naked eye. Objective and rapid technologies to detect bee mites are required. Image processing considerably improves detection performance. Therefore, this study proposes an image-processing method that can increase the detection performance of bee mites. A keypoint detection algorithm was implemented to identify keypoint location and frequencies in images of bees and bee mites. These parameters were analyzed to determine the rational measurement distance and image-processing. The change in the number of keypoints was analyzed by applying five-color model conversion, histogram normalization, and two-histogram equalization. The performance of the keypoints was verified by matching images with infested bees and mites. Among 30 given cases of image processing, the method applying normalization and equalization in the RGB color model image produced consistent quality data and was the most valid keypoint. Optimal image processing worked effectively in the measured 300 mm data in the range 300–1100 mm. The results of this study show that diverse image-processing techniques help to enhance the quality of bee mite detection significantly. This approach can be used in conjunction with an object detection deep-learning algorithm to monitor bee mites and diseases.

Keywords: bee mite; image processing; keypoint detection; image matching (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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