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http://dx.doi.org/10.7744/kjoas.20200062

Development of weight prediction 2D image technology using the surface shape characteristics of strawberry cultivars  

Yoo, Hyeonchae (National Institute of Agricultural Sciences, Rural Development Administration)
Lim, Jongguk (National Institute of Agricultural Sciences, Rural Development Administration)
Kim, Giyoung (National Institute of Agricultural Sciences, Rural Development Administration)
Kim, Moon Sung (USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center)
Kang, Jungsook (National Institute of Agricultural Sciences, Rural Development Administration)
Seo, Youngwook (National Institute of Agricultural Sciences, Rural Development Administration)
Lee, Ah-yeong (National Institute of Agricultural Sciences, Rural Development Administration)
Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
Hong, Soon-Jung (Korea National College of Agriculture and Fisheries)
Mo, Changyeun (Department of Biosystems Engineering, College of Agricultural and Life Science, Kangwon National University)
Publication Information
Korean Journal of Agricultural Science / v.47, no.4, 2020 , pp. 753-767 More about this Journal
Abstract
The commercial value of strawberries is affected by various factors such as their shape, size and color. Among them, size determined by weight is one of the main factors determining the quality grade of strawberries. In this study, image technology was developed to predict the weight of strawberries using the shape characteristics of strawberry cultivars. For realtime weight measurements of strawberries in transport, an image measurement system was developed for weight prediction with a charge coupled device (CCD) color camera and a conveyor belt. A strawberry weight prediction algorithm was developed for three cultivars, Maehyang, Sulhyang, and Ssanta, using the number of pixels in the pulp portion that measured the strawberry weight. The discrimination accuracy (R2) of the weight prediction models of the Maeyang, Sulhyang and Santa cultivars was 0.9531, 0.951 and 0.9432, respectively. The discriminative accuracy (R2) and measurement error (RMSE) of the integrated weight prediction model of the three cultivars were 0.958 and 1.454 g, respectively. These results show that the 2D imaging technology considering the shape characteristics of strawberries has the potential to predict the weight of strawberries.
Keywords
2D image; image process; shape; strawberry; weight prediction;
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Times Cited By KSCI : 12  (Citation Analysis)
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