Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
권호 표지
DOI QR코드

DOI QR Code

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)
  • Received : 2020.07.24
  • Accepted : 2020.09.16
  • Published : 2020.12.01
Download PDF
⟨ Previous Next ⟩

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

Acknowledgement

본 결과물은 2019년도 강원대학교 대학회계 학술연구조성사업(520190071)와 농림축산식품부의 재원으로 농림식품기술기획평가원의 첨단농기계산업화기술개발 사업 (320031-03-1-HD020)의 지원을 받아 연구되었음.

References

  1. Ahmed MR, Yasmin J, Collins W, Lohumi S, Cho BK. 2019. Assessment of the morphological structure of watermelon and muskmelon seeds as related to viability. Journal of Biosystems Engineering 44:77-86. doi.org/10.1007/s42853-019-00018-w
  2. Ariza MT, Reboredo-Rodriguez P, Mazzoni L, Forbes-Hernandez TY, Giampieri F, Afrin S, Mezzetti B. 2016. Strawberry achenes are an important source of bioactive compounds for human health. International Journal of Molecular Sciences 17:1-14. doi.org/10.3390/ijms17071103
  3. Brosnan T, Sun DW. 2002. Inspection and grading of agricultural and food products by computer vision systems - A review. Computers and Electronics in Agriculture 36:193-213. doi.org/10.1016/S0168-1699(02)00101-1
  4. Cordenunsi BR, Genovese MI, do Nascimento JRO, Hassimotto NMA, dos Santos RJ, Lajolo FM. 2005. Effects of temperature on the chemical composition and antioxidant activity of three strawberry cultivars. Food Chemistry 91:113-121. doi.org/10.1016/j.foodchem.2004.05.054
  5. Hong SJ, Lee AY, Han YH, Park JM, So JD, Kim GS. 2018. Rancidity prediction of soybean oil by using near-infrared spectroscopy techniques. Journal of Biosystems Engineering 43:219-228. https://doi.org/10.5307/jbe.2018.43.3.219
  6. Joshi R, Cho BK, Lohumi S, Joshi R, Lee J, Lee H, Mo C. 2019. Evaluation of benzene residue in edible oils using Fourier transform infrared (FTIR) spectroscopy. Korean Journal of Agricultural Science 46:257-271. https://doi.org/10.7744/KJOAS.20190012
  7. Kang YS, Ryu CS, Kim SH, Jun SR, Jang SH, Park JW, Song HY. 2018. Yield prediction of chinese cabbage (brassicaceae) using broadband multispectral imagery mounted unmanned aerial system in the air and narrowband hyperspectral imagery on the ground. Journal of Biosystems Engineering 43:138-147. doi.org/10.5307/JBE.2018.43.2.138
  8. Lee A, Kim SY, Hong SJ, Han Y, Choi Y, Kim M, Kim G. 2019. Phenotypic analysis of fruit crops water stress using infrared thermal imaging. Journal of Biosystems Engineering 44:87-94. doi.org/10.1007/s42853-019-00020-2
  9. Lee YJ, Kim KD, Lee HS, Shin BS. 2018. Vision based potato detection and counting system for yield monitoring. Journal of Biosystems Engineering 43:103-109. https://doi.org/10.5307/JBE.2018.43.2.103
  10. Lim JG, Kim G, Mo CY, Choi I. 2015. Development of falling-type dried-persimmon weight sorting system utilizing loadcell. Journal of Biosystems Engineering 40:327-334. https://doi.org/10.5307/JBE.2015.40.4.327
  11. Liming X, Yanchao Z. 2010. Automated strawberry grading system based on image processing. Computers and Electronics in Agriculture 71:32-39. doi.org/10.1016/j.compag.2009.09.013
  12. Menguito P. 2000. Study on sorting system for strawberry using machine vision (part 2). Journal of the Japanese Society of Agricultural Machinery 62:101-110. doi.org/10.11357/jsam1937.62.2_101
  13. Mo CY, Lim JG, Kwon SW, Lim DK, Kim MS, Kim GY, Cho BK. 2017. Hyperspectral imaging and partial least square discriminant analysis for geographical origin discrimination of white rice. Journal of Biosystems Engineering 42:293-300. https://doi.org/10.5307/JBE.2017.42.4.293
  14. Ning XF, Gong YJ, Chen YL, Li H. 2018. Construction of a ginsenoside content-predicting model based on hyperspectral imaging. Journal of Biosystems Engineering 43:369-378. doi.org/10.5307/JBE.2018.43.4.369
  15. Oszmianski J, Wojdylo A. 2009. Comparative study of phenolic content and antioxidant activity of strawberry puree, clear, and cloudy juices. European Food Research and Technology 228:623-631. doi.org/10.1007/s00217-008-0971-2
  16. Podsedek A. 2007. Natural antioxidants and antioxidant capacity of brassica vegetables: A review. LWT-Food Science and Technology 40:1-11. doi.org/10.1016/j.lwt.2005.07.023
  17. Sarkar TK, Ryu CS, Kang YS, Kim SH, Jeon SR, Jang SH, Kim HJ. 2018. Integrating UAV remote sensing with gis for predicting rice grain protein. Journal of Biosystems Engineering 43:148-159. doi.org/10.5307/JBE.2018.43.2.148
  18. Seo YW, Lee HS, Bae HJ, Park ES, Lim HS, Kim MS, Cho BK. 2019. Optimized multivariate analysis for the discrimination of cucumber green mosaic mottle virus-infected watermelon seeds based on spectral imaging. Journal of Biosystems Engineering 44:95-102. doi.org/10.1007/s42853-019-00019-9
  19. Teoh CC, Syaifudin ARM. 2007. Image processing and analysis techniques for estimating weight of Chokanan mangoes. Journal of Tropical Agriculture and Food Science 35:183-190.
  20. Velde FVD, Tarola A, Guemes D, Pirovani M. 2013. Bioactive compounds and antioxidant capacity of camarosa and selva strawberries (Fragaria x ananassa Duch.). Foods 2:120-131. doi.org/10.3390/foods2020120