{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:26:24Z","timestamp":1778603184426,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["20004055"],"award-info":[{"award-number":["20004055"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advances in machine learning and artificial intelligence have led to many promising solutions for challenging issues in agriculture. One of the remaining challenges is to develop practical applications, such as an automatic sorting system for after-ripening crops such as tomatoes, according to ripeness stages in the post-harvesting process. This paper proposes a novel method for detecting tomato ripeness by utilizing multiple streams of convolutional neural network (ConvNet) and their stochastic decision fusion (SDF) methodology. We have named the overall pipeline as SDF-ConvNets. The SDF-ConvNets can correctly detect the tomato ripeness by following consecutive phases: (1) an initial tomato ripeness detection for multi-view images based on the deep learning model, and (2) stochastic decision fusion of those initial results to obtain the final classification result. To train and validate the proposed method, we built a large-scale image dataset collected from a total of 2712 tomato samples according to five continuous ripeness stages. Five-fold cross-validation was used for a reliable evaluation of the performance of the proposed method. The experimental results indicate that the average accuracy for detecting the five ripeness stages of tomato samples reached 96%. In addition, we found that the proposed decision fusion phase contributed to the improvement of the accuracy of the tomato ripeness detection.<\/jats:p>","DOI":"10.3390\/s21030917","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T09:25:22Z","timestamp":1611912322000},"page":"917","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Stochastic Decision Fusion of Convolutional Neural Networks for Tomato Ripeness Detection in Agricultural Sorting Systems"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6225-8080","authenticated-orcid":false,"given":"KwangEun","family":"Ko","sequence":"first","affiliation":[{"name":"Korea Institute of Industrial Technology, 143 Hanggaulro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-5310","authenticated-orcid":false,"given":"Inhoon","family":"Jang","sequence":"additional","affiliation":[{"name":"Korea Institute of Industrial Technology, 143 Hanggaulro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Korea"}]},{"given":"Jeong Hee","family":"Choi","sequence":"additional","affiliation":[{"name":"Korea Food Research Institute, 245, Nongsaengmyeong-ro, Iseo-myeon, Wanju-Gun 55365, Jeollabuk-do, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4806-2046","authenticated-orcid":false,"given":"Jeong Ho","family":"Lim","sequence":"additional","affiliation":[{"name":"Korea Food Research Institute, 245, Nongsaengmyeong-ro, Iseo-myeon, Wanju-Gun 55365, Jeollabuk-do, Korea"}]},{"given":"Da Uhm","family":"Lee","sequence":"additional","affiliation":[{"name":"Korea Food Research Institute, 245, Nongsaengmyeong-ro, Iseo-myeon, Wanju-Gun 55365, Jeollabuk-do, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.scienta.2016.02.016","article-title":"Spectral evaluation of apple fruit ripening and pigment content alteration","volume":"201","author":"Nagy","year":"2016","journal-title":"Sci. Hortic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1002\/jsfa.1370","article-title":"Effects of environmental factors and agricultural techniques on antioxidant content of tomatoes","volume":"83","author":"Dumas","year":"2003","journal-title":"J. Sci. Food Agric."},{"key":"ref_3","first-page":"712","article-title":"Tomato (Lycopersicon esculentum Mill.) fruit quality and physiological parameters at different ripening stages of Lithuanian cultivars","volume":"7","author":"Viskelis","year":"2009","journal-title":"Agron. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1021\/jf072196t","article-title":"How Does Tomato Quality (Sugar, Acid, and Nutritional Quality) Vary with Ripening Stage, Temperature, and Irradiance?","volume":"56","author":"Gautier","year":"2008","journal-title":"J. Agric. Food Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1146\/annurev-genet-110410-132507","article-title":"Genetics and Control of Tomato Fruit Ripening and Quality Attributes","volume":"45","author":"Klee","year":"2011","journal-title":"Annu. Rev. Genet."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/S0925-5214(02)00012-1","article-title":"Ethylene perception is required for the expression of tomato ripening-related genes and associated physiological changes even at advanced stages of ripening","volume":"26","author":"Hoeberichts","year":"2002","journal-title":"Postharvest Biol. Technol."},{"key":"ref_7","first-page":"49","article-title":"Optimization of Ethylene inhibitor-mediated controlled ripening of tomato (Solanum lycopersicum L.)","volume":"6","author":"Barua","year":"2018","journal-title":"Adv. Agric. Sci."},{"key":"ref_8","first-page":"1168","article-title":"Automatic food detection in egocentric images using artificial intelligence technology","volume":"22","author":"Jia","year":"2019","journal-title":"Public Health Nutr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1587\/transinf.2017MVP0027","article-title":"Image-based food calorie estimation using recipe information","volume":"101","author":"Ege","year":"2018","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kaur, G., Kaushik, A., and Sharma, S. (2019). Cooking is creating emotion: A study on hinglish sentiments of youtube cookery channels using semi-supervised approach. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3030037"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.biosystemseng.2016.05.001","article-title":"Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis","volume":"148","author":"Zhao","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, G., Mao, S., and Kim, J.H. (2019). A mature-tomato detection algorithm using machine learning and color analysis. Sensors (Switzerland), 19.","DOI":"10.3390\/s19092023"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"154683","DOI":"10.1109\/ACCESS.2019.2949343","article-title":"Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s00138-020-01081-6","article-title":"Detection of tomato organs based on convolutional neural network under the overlap and occlusion backgrounds","volume":"31","author":"Sun","year":"2020","journal-title":"Mach. Vis. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.asoc.2015.07.009","article-title":"Fuzzy classification of pre-harvest tomatoes for ripeness estimation {\\textendash} An approach based on automatic rule learning using decision tree","volume":"36","author":"Goel","year":"2015","journal-title":"Appl. Soft Comput. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1892","DOI":"10.1016\/j.eswa.2014.09.057","article-title":"Using machine learning techniques for evaluating tomato ripeness","volume":"42","author":"Hassanien","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2018.01.011","article-title":"A methodology for fresh tomato maturity detection using computer vision","volume":"146","author":"Wan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","article-title":"Handcrafted vs. non-handcrafted features for computer vision classification","volume":"71","author":"Nanni","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","unstructured":"Li, C., Cao, Q., and Guo, F. (2009). A method for color classification of fruits based on machine vision. WSEAS Trans. Syst., 8."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.procs.2016.03.055","article-title":"Lakshmana Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry","volume":"79","author":"Arakeri","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (2014). Two-Stream Convolutional Networks for Action Recognition in Videos. Advances in Neural Information Processing Systems 27, Curran Associates, Inc."},{"key":"ref_23","unstructured":"Redmon, J., and Farhadi, A. (2021, January 14). YOLOv3: An Incremental Improvement. pjreddie.com, Available online: https:\/\/pjreddie.com\/media\/files\/papers\/YOLOv3.pdf."},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_26","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"2","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process."},{"key":"ref_27","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 27). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 1137\u20131149.","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., and Girshick, R. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_30","unstructured":"Yang, Z., Yu, Y., You, C., Steinhardt, J., and Ma, Y. (2020). Rethinking bias-variance trade-off for generalization of neural networks. arXiv."},{"key":"ref_31","unstructured":"Redmon, J. (2021, January 14). Darknet: Open Source Neural Networks in C. Available online: https:\/\/pjreddie.com\/darknet\/."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103090","DOI":"10.1016\/j.micpro.2020.103090","article-title":"A Microcontroller based Machine Vision Approach for Tomato Grading and Sorting using SVM Classifier","volume":"76","author":"Kumar","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kuznetsova, A., Maleva, T., and Soloviev, V. (2020, January 3\u20134). Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close-Up Images. Proceedings of the International Symposium on Neural Networks, Cairo, Egypt.","DOI":"10.1007\/978-3-030-64221-1_20"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/917\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:17:14Z","timestamp":1760159834000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/917"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030917"],"URL":"https:\/\/doi.org\/10.3390\/s21030917","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,29]]}}}