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7 June 2024 Bee mite detection based on deep learning using hyperspectral imaging
Hong-Gu Lee, Jeong Yong Shin, Su-bae Kim, Changyeun Mo
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Proceedings Volume PC13060, Sensing for Agriculture and Food Quality and Safety XVI; PC1306006 (2024) https://doi.org/10.1117/12.3015331
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States
Abstract
The purpose of this study is to develop a bee mite detection model using hyperspectral images of bee combs and deep learning-based recognition algorithms. The hyperspectral image has a resolution of 510 × 270 and consist of 15 wavebands in the ranging from 611 nm to 850 nm. Image processing was applied to preprocess data and used for data augmentation. Convolutional Neural Network (CNN) was used to develop the bee and bee mite detection models. The developed bee mite detection model was combined with an algorithm for localization to detect honey bees in hyperspectral bee comb images. Consequently, the bee mite detection model using snapshot hyperspectral image was developed to recognize the bee mite.
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Hong-Gu Lee, Jeong Yong Shin, Su-bae Kim, and Changyeun Mo "Bee mite detection based on deep learning using hyperspectral imaging", Proc. SPIE PC13060, Sensing for Agriculture and Food Quality and Safety XVI, PC1306006 (7 June 2024); https://doi.org/10.1117/12.3015331
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KEYWORDS
Hyperspectral imaging
Algorithm development
Detection and tracking algorithms
Feature extraction
Optical inspection
Light sources
Nondestructive evaluation
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