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13 June 2023 Bee Mite Detection Model Combined YOLO and Image Processing (Conference Presentation)
Doojin Song, Seung-Woo Chun, Soo-Hwan Park, Min-Jee Kim, Changyeun Mo
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Proceedings Volume PC12545, Sensing for Agriculture and Food Quality and Safety XV; PC1254502 (2023) https://doi.org/10.1117/12.2664920
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
In this study, the optimal distance between the light source and the sensor by each apple size was investigated for soluble solid content (SSC) measurement, and 1D-Convolutional Neural Network (CNN) SSC models were developed at that distance. The visible/near-infrared transmittance spectra of apple in the range of 400 to 1100 nm were measured using a 100W halogen light source. The distance between the light source and the sensor was set at three levels, which had less impact on the size of the apple investigated in the previous study. The transmission spectra of the fruit were measured at the distance of each level by size, and the SSC was also measured. 1D- CNN was used to develop SSC estimation models. The results of this study showed that 1D-CNN technology could improve the SSC measurement performance of apples. In the future, these deep learning results can be applied to a high-performance online non-destructive fruit sorter.
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Doojin Song, Seung-Woo Chun, Soo-Hwan Park, Min-Jee Kim, and Changyeun Mo "Bee Mite Detection Model Combined YOLO and Image Processing (Conference Presentation)", Proc. SPIE PC12545, Sensing for Agriculture and Food Quality and Safety XV, PC1254502 (13 June 2023); https://doi.org/10.1117/12.2664920
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KEYWORDS
Near infrared spectroscopy
Neural networks
Solids
Spectroscopy
Visible radiation
Solid modeling
Light sources
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