Grading method for tomato multi-view shape using machine vision
Keywords:
machine vision, centroid distance, multi-view, tomato shape, grading methodAbstract
Owing to the requirements of a high yield and high-quality tomatoes, tomato grading is important-particularly for fruit morphology, and accuracy has become the focus of attention. Machine vision provides a fast and nondestructive manner to address this demand. In this study, the gamma correction method was used for preprocessing to enhance the edge information of tomatoes, and Otsu’s method was used to eliminate the tomato-image background in the A-component image under the LAB color model. On this basis, two levels of exploration were conducted. First, new evaluation indices were proposed for tomato shapes from different views. For the top view, two shape-evaluation indices were established: the area ratio of the maximum inscribed circle to the maximum circumscribed circle and the dispersion of the contour centroid distance (range and coefficient of variation), the highest accuracy was 94%. For the side view, the difference between the maximum and minimum centroid distances in the contour was established as a shape index, the highest accuracy was 91.91%. Second, an evaluation method based on multi-view fusion was developed by combining the advantage indices for different views. The classification accuracy reached 96%, with the highest identification accuracy of unqualified tomatoes. The results show that the proposed evaluation method combining top views (dispersion of centroid distance) with side views (difference between maximum and minimum centroid distances) is effective for classifying tomatoes. Key words: machine vision, centroid distance, multi-view, tomato shape, grading method DOI: 10.25165/j.ijabe.20231606.7768 Citation: Chen L P, He T T, Li Z W, Zheng W G, An S W, Zhang Z L L. Grading method for tomato multi-view shape using machine vision. Int J Agric & Biol Eng, 2023; 16(6): 184–196.References
[1] Zhang H. According to the current situation of the global tomato industry, there is still a big gap between China’s tomato production and foreign countries. China Economic Information Network. Available: https://www.huaon.com/channel/trend/729400.html. Accessed on [2021-07-06]. (in Chinese)
[2] NYT940-2006. Grades and specifications of tomatoes. 2006; 8p. (in Chinese)
[3] Costa C, Antonucci F, Pallottino F, Aguzzi J, Sun D W, Menesatti P. Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food and Bioprocess Technology, 2011; 4(5): 673–692.
[4] Lu H S, Wang F J, Liu XL, Wu Y Y. Rapid assessment of tomato ripeness using visible/near-infrared spectroscopy and machine vision. Food Analytical Methods, 2017; 10(6): 1721–1726.
[5] Wang A C, Fu X P, Xie L J. Application of visible/near-infrared spectroscopy combined with machine vision technique to evaluate the ripeness of melons ( Cucumis melo L.). Food Analytical Methods, 2015; 8(6): 1403–1412.
[6] Sarkar T, Mukherjee A< Chatterjee K, Ermolaev V, Piotrovsky D, Vlasova K, et al. Edge detection aided geometrical shape analysis of Indian gooseberry ( Phyllanthus emblica) for freshness classification. Food Analytical Methods, 2022; 15(6): 1490–1507.
[7] Saglam C, Cetin N. Prediction of pistachio ( Pistacia vera L.) mass based on shape and size attributes by using machine learning algorithms. Food Analytical Methods, 2022; 15(3): 739–750.
[8] Sun K, Li Y, Peng J, Tu K, Pan L Q. Surface gloss evaluation of apples based on computer vision and support vector machine method. Food Analytical Methods, 2017; 10(8): 2800–2806.
[9] Bhargava A, Barisal A. Automatic detection and grading of multiple fruits by machine learning. Food Analytical Methods, 2020; 13(3): 751–761.
[10] Sun D-W, Costa C, Menesatti P. Advantages of using quantitative shape descriptors in protocols for plant cultivar and postharvest product quality assessment. Food and Bioprocess Technology, 2011; 5(1): 1–2.
[11] Cai Y. Research on quality inspection and grading of tomatoes based on machine vision. Master dissertation. Guangzhou: Sun Yat-sen University, 2010; 83p. (in Chinese)
[12] Wang H Z. Image recognition method of tomato appearance characteristics. Master dissertation. Yangling: Northwest A&F University, 2018; 52p. (in Chinese)
[13] Arjenaki O O, Moghaddam P A, Motlagh A M. Tomato sorting based on maturity, surface defects and shape by using of machine vision system. In: Proceedings of the CIGR-AGENG, 2012.
[14] Zhang Q, Zuo X J, Lin G C, Sun Y H. Image feature extraction and online grading method for weight and shape of strawberry. Journal of System Simulation, 2019; 31(1): 7–15. (in Chinese)
[15] Wang J T, Mu H T. An improved Hu moment of tomato shape feature extraction based on FFT. Machinery Design & Manufacture, 2019; 5: 228–231.
[16] Ying Y B, Gui J S, Rao X Q. Fruit shape classification based on Zernike moments. Journal of Jiangsu University (Natural Science Edition), 2007; 28(1): 1–3, 67. (in Chinese)
[17] Hao M, Ma S S, Hao X D. Potato shape detection based on Zernike moments. Transactions of the CSAE, 2010; 26(2): 347–350. (in Chinese)
[18] Gui J S, Zhang Q, Hao L, Bao X A. Apple shape classification method based on wavelet moment. Sensors & Transducers Journal, 2014; 178(9): 182–187.
[19] Li C Y, Cao Q X. Extraction method of shape feature for vegetables based on depth image. Transactions of the CSAM, 2012; 43(S1): 242–245. (in Chinese)
[20] Yuan L, Tu X Y, Ju G, Liu Z B, Wu J Q. Tomato real-time classification based on machine vision detection system design. Journal of Xinjiang University (Natural Science Edition), 2017; 34(1): 11–16. (in Chinese)
[21] Liu L, Li Z K, Lan Y F, Shi Y G, Cui Y J. Design of a tomato classifier based on machine vision. PLoS ONE, 2019; 14(7): e0219803.
[22] Yao Y F, Liu F, Zhang H D, Li C, Zhou H J, Wang M. Research on octagon color and fruit shape recognition based on machine vision. Journal of Agricultural Science and Technology, 2021; 23(11): 110–120. (in Chinese)
[23] Antonucci F, Costa C, Pallottino F, Paglia G, Rimatori V, De Giorgio D, Menesatti. Quantitative method for shape description of almond cultivars ( Prunus amygdalus Batsch). Food and Bioprocess Technology, 2012; 5(2): 768–785.
[24] Ishikawa T, Hayashi A, Nagamatsu S, Kyutoku Y, Dan I, Wada T, et al. Classification of strawberry fruit shape by machine learning. ISPRS - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018; 42(2): 463–470.
[25] Tigistu T, Abebe G. Classification of rose flowers based on Fourier descriptors and color moments. Multimedia Tools and Applications, 2021; 80(30): 36143–36157.
[26] Xiao A L, Pan B. Identification of the shape of Chinese date based on Labelling method and extremum of polar radius. Journal of Agricultural Mechanization Research, 2015; 37(7): 61–65. (in Chinese)
[27] Pei Y K, Ye J M, Jiang Y C, Lian M Y, Han X X, Gu Y. The cherry shape and size detection technology based on machine vision. The Food Industry, 2020; 41(8): 199–202. (in Chinese)
[28] Li Y, Shen J, Xie H, Gao G Y, Liu J X, Liu J. Detection and grading method of pomelo shape based on contour coordinate transformation and fitting. Smart Agriculture, 2021; 3(1): 86–95. (in Chinese)
[29] Guo H. Research on pummelo quality detection based on machine vision. PhD dissertation. Beijing: China Agricultural University, 2015; 133p. (in Chinese)
[30] DB11/T 907.1-2012. Identification experiment code for vegetable varieties Part 1: Solanaceous fruits. 2012.
[31] Yin R, Gao J, Meng G F, Pan Y Y. A method of tomato shape classification with the application of circularity. Journal of Changshu Institute of Technology, 2013; 27(4): 100–103. (in Chinese)
[32] Li J, Wu C P, Liu M H, Chen J Y, Zheng J H, Zhang Y F, et al. Detection of shape characteristics of kiwifruit based on hyperspectral imaging technology. Spectroscopy and Spectral Analysis, 2020; 40(8): 2564–2570. (in Chinese)
[33] Mon T, ZarAung N. Vision based volume estimation method for automatic mango grading system. Biosystems Engineering, 2020; 198: 338–349.
[2] NYT940-2006. Grades and specifications of tomatoes. 2006; 8p. (in Chinese)
[3] Costa C, Antonucci F, Pallottino F, Aguzzi J, Sun D W, Menesatti P. Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food and Bioprocess Technology, 2011; 4(5): 673–692.
[4] Lu H S, Wang F J, Liu XL, Wu Y Y. Rapid assessment of tomato ripeness using visible/near-infrared spectroscopy and machine vision. Food Analytical Methods, 2017; 10(6): 1721–1726.
[5] Wang A C, Fu X P, Xie L J. Application of visible/near-infrared spectroscopy combined with machine vision technique to evaluate the ripeness of melons ( Cucumis melo L.). Food Analytical Methods, 2015; 8(6): 1403–1412.
[6] Sarkar T, Mukherjee A< Chatterjee K, Ermolaev V, Piotrovsky D, Vlasova K, et al. Edge detection aided geometrical shape analysis of Indian gooseberry ( Phyllanthus emblica) for freshness classification. Food Analytical Methods, 2022; 15(6): 1490–1507.
[7] Saglam C, Cetin N. Prediction of pistachio ( Pistacia vera L.) mass based on shape and size attributes by using machine learning algorithms. Food Analytical Methods, 2022; 15(3): 739–750.
[8] Sun K, Li Y, Peng J, Tu K, Pan L Q. Surface gloss evaluation of apples based on computer vision and support vector machine method. Food Analytical Methods, 2017; 10(8): 2800–2806.
[9] Bhargava A, Barisal A. Automatic detection and grading of multiple fruits by machine learning. Food Analytical Methods, 2020; 13(3): 751–761.
[10] Sun D-W, Costa C, Menesatti P. Advantages of using quantitative shape descriptors in protocols for plant cultivar and postharvest product quality assessment. Food and Bioprocess Technology, 2011; 5(1): 1–2.
[11] Cai Y. Research on quality inspection and grading of tomatoes based on machine vision. Master dissertation. Guangzhou: Sun Yat-sen University, 2010; 83p. (in Chinese)
[12] Wang H Z. Image recognition method of tomato appearance characteristics. Master dissertation. Yangling: Northwest A&F University, 2018; 52p. (in Chinese)
[13] Arjenaki O O, Moghaddam P A, Motlagh A M. Tomato sorting based on maturity, surface defects and shape by using of machine vision system. In: Proceedings of the CIGR-AGENG, 2012.
[14] Zhang Q, Zuo X J, Lin G C, Sun Y H. Image feature extraction and online grading method for weight and shape of strawberry. Journal of System Simulation, 2019; 31(1): 7–15. (in Chinese)
[15] Wang J T, Mu H T. An improved Hu moment of tomato shape feature extraction based on FFT. Machinery Design & Manufacture, 2019; 5: 228–231.
[16] Ying Y B, Gui J S, Rao X Q. Fruit shape classification based on Zernike moments. Journal of Jiangsu University (Natural Science Edition), 2007; 28(1): 1–3, 67. (in Chinese)
[17] Hao M, Ma S S, Hao X D. Potato shape detection based on Zernike moments. Transactions of the CSAE, 2010; 26(2): 347–350. (in Chinese)
[18] Gui J S, Zhang Q, Hao L, Bao X A. Apple shape classification method based on wavelet moment. Sensors & Transducers Journal, 2014; 178(9): 182–187.
[19] Li C Y, Cao Q X. Extraction method of shape feature for vegetables based on depth image. Transactions of the CSAM, 2012; 43(S1): 242–245. (in Chinese)
[20] Yuan L, Tu X Y, Ju G, Liu Z B, Wu J Q. Tomato real-time classification based on machine vision detection system design. Journal of Xinjiang University (Natural Science Edition), 2017; 34(1): 11–16. (in Chinese)
[21] Liu L, Li Z K, Lan Y F, Shi Y G, Cui Y J. Design of a tomato classifier based on machine vision. PLoS ONE, 2019; 14(7): e0219803.
[22] Yao Y F, Liu F, Zhang H D, Li C, Zhou H J, Wang M. Research on octagon color and fruit shape recognition based on machine vision. Journal of Agricultural Science and Technology, 2021; 23(11): 110–120. (in Chinese)
[23] Antonucci F, Costa C, Pallottino F, Paglia G, Rimatori V, De Giorgio D, Menesatti. Quantitative method for shape description of almond cultivars ( Prunus amygdalus Batsch). Food and Bioprocess Technology, 2012; 5(2): 768–785.
[24] Ishikawa T, Hayashi A, Nagamatsu S, Kyutoku Y, Dan I, Wada T, et al. Classification of strawberry fruit shape by machine learning. ISPRS - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018; 42(2): 463–470.
[25] Tigistu T, Abebe G. Classification of rose flowers based on Fourier descriptors and color moments. Multimedia Tools and Applications, 2021; 80(30): 36143–36157.
[26] Xiao A L, Pan B. Identification of the shape of Chinese date based on Labelling method and extremum of polar radius. Journal of Agricultural Mechanization Research, 2015; 37(7): 61–65. (in Chinese)
[27] Pei Y K, Ye J M, Jiang Y C, Lian M Y, Han X X, Gu Y. The cherry shape and size detection technology based on machine vision. The Food Industry, 2020; 41(8): 199–202. (in Chinese)
[28] Li Y, Shen J, Xie H, Gao G Y, Liu J X, Liu J. Detection and grading method of pomelo shape based on contour coordinate transformation and fitting. Smart Agriculture, 2021; 3(1): 86–95. (in Chinese)
[29] Guo H. Research on pummelo quality detection based on machine vision. PhD dissertation. Beijing: China Agricultural University, 2015; 133p. (in Chinese)
[30] DB11/T 907.1-2012. Identification experiment code for vegetable varieties Part 1: Solanaceous fruits. 2012.
[31] Yin R, Gao J, Meng G F, Pan Y Y. A method of tomato shape classification with the application of circularity. Journal of Changshu Institute of Technology, 2013; 27(4): 100–103. (in Chinese)
[32] Li J, Wu C P, Liu M H, Chen J Y, Zheng J H, Zhang Y F, et al. Detection of shape characteristics of kiwifruit based on hyperspectral imaging technology. Spectroscopy and Spectral Analysis, 2020; 40(8): 2564–2570. (in Chinese)
[33] Mon T, ZarAung N. Vision based volume estimation method for automatic mango grading system. Biosystems Engineering, 2020; 198: 338–349.
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Published
2024-02-06
How to Cite
Chen, L., He, T., Li, Z., Zheng, W., An, S., & Zhangzhong, L. (2024). Grading method for tomato multi-view shape using machine vision. International Journal of Agricultural and Biological Engineering, 16(6), 184–196. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7768
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Information Technology, Sensors and Control Systems
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