{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:00:59Z","timestamp":1732039259960},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["CREST Program \u201cKnowledge Discovery by Constructing AgriBigData\u201d (JPMJCR1512)","SICORP Program \u201cData Science-based Farming Support System for Sustainable Crop Production under Climatic Change\u201d"],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and variable lighting conditions is highly desired. In this study, we build a tomato detection model to automatically detect intact green tomatoes regardless of occlusions or fruit growth stage using deep learning approaches. The tomato detection model used faster region-based convolutional neural network (R-CNN) with Resnet-101 and transfer learned from the Common Objects in Context (COCO) dataset. The detection on test dataset achieved high average precision of 87.83% (intersection over union \u2265 0.5) and showed a high accuracy of tomato counting (R2 = 0.87). In addition, all the detected boxes were merged into one image to compile the tomato location map and estimate their size along one row in the greenhouse. By tomato detection, counting, location and size estimation, this method shows great potential for ripeness and yield prediction.<\/jats:p>","DOI":"10.3390\/s20102984","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T15:42:02Z","timestamp":1590421322000},"page":"2984","source":"Crossref","is-referenced-by-count":81,"title":["Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-4679-6393","authenticated-orcid":false,"given":"Yue","family":"Mu","sequence":"first","affiliation":[{"name":"Plant Phenomics Research Center, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China"},{"name":"International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, The University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan"}]},{"given":"Tai-Shen","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2123-4354","authenticated-orcid":false,"given":"Seishi","family":"Ninomiya","sequence":"additional","affiliation":[{"name":"Plant Phenomics Research Center, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China"},{"name":"International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, The University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3017-5464","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, The University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4238\/gmr16029540","article-title":"Industrial tomato lines: Morphological properties and productivity","volume":"16","author":"Peixoto","year":"2017","journal-title":"Genet. Mol. Res."},{"key":"ref_2","unstructured":"Food and Agriculture Organization of the United Nations (2019, October 29). FAOSTAT. Available online: http:\/\/www.fao.org\/faostat\/en\/#data\/QC."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s00299-018-2283-8","article-title":"Can the world\u2019s favorite fruit, tomato, provide an effective biosynthetic chassis for high-value metabolites?","volume":"37","author":"Li","year":"2018","journal-title":"Plant Cell Rep."},{"key":"ref_4","unstructured":"Food and Agriculture Organization of the United Nations (2019, October 29). Tomato | Land & Water. Available online: http:\/\/www.fao.org\/land-water\/databases-and-software\/crop-information\/tomato\/en\/."},{"key":"ref_5","unstructured":"Sinivasan, R. (2010). Safer Tomato Production Methods: A Field Guide for Soil Fertility and Pest Management, AVRDC-The World Vegetable Center."},{"key":"ref_6","unstructured":"Rutledge, A.D. (2020, April 16). Commercial Greenhouse Tomato Production. Available online: https:\/\/extension.tennessee.edu\/publications\/Documents\/pb1609.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2014Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1006\/anbo.1998.0804","article-title":"A Compartment Model of the Effect of Early-Season Temperatures on Potential Size and Growth of \u201cDelicious\u201d Apple Fruits","volume":"83","author":"Austin","year":"1999","journal-title":"Ann. Bot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Malik, Z., Ziauddin, S., Shahid, A.R., and Safi, A. (2016). Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation. IJACSA Int. J. Adv. Comput. Sci. Appl., 7.","DOI":"10.14569\/IJACSA.2016.070569"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.jfoodeng.2004.11.020","article-title":"Physical and mechanical properties of mango during growth and storage for determination of maturity","volume":"72","author":"Jha","year":"2006","journal-title":"J. Food Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MPRV.2018.2873849","article-title":"Pervasive Agriculture: IoT-Enabled Greenhouse for Plant Growth Control","volume":"17","author":"Somov","year":"2018","journal-title":"IEEE Pervasive Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.robot.2019.01.019","article-title":"Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision","volume":"114","author":"Ling","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_13","first-page":"9","article-title":"Detection of red tomato on plants using image processing techniques","volume":"2","author":"Khoshroo","year":"2014","journal-title":"Agric. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12191","DOI":"10.3390\/s140712191","article-title":"On plant detection of intact tomato fruits using image analysis and machine learning methods","volume":"14","author":"Yamamoto","year":"2014","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.compag.2018.07.011","article-title":"Immature green citrus fruit detection using color and thermal images","volume":"152","author":"Gan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., and McCool, C. (2016). DeepFruits: A fruit detection system using deep neural networks. Sensors, 16.","DOI":"10.3390\/s16081222"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.compag.2014.10.016","article-title":"Detecting citrus fruits and occlusion recovery under natural illumination conditions","volume":"110","author":"Lu","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","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_19","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, 19.","DOI":"10.3390\/s19092023"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rahnemoonfar, M., and Sheppard, C. (2017). Deep Count: Fruit counting based on deep simulated learning. Sensors, 17.","DOI":"10.3390\/s17040905"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/LRA.2017.2651944","article-title":"Counting apples and oranges with deep learning: A data-driven approach","volume":"2","author":"Chen","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bargoti, S., and Underwood, J. (June, January 29). Deep fruit detection in orchards. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989417"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_26","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Z., Walsh, K., and Verma, B. (2017). On-Tree Mango Fruit Size Estimation Using RGB-D Images. Sensors, 17.","DOI":"10.3390\/s17122738"},{"key":"ref_28","unstructured":"Schillaci, G., Pennisi, A., Franco, F., and Longo, D. (2012, January 3\u20136). Detecting Tomato Crops in Greenhouses Using a Vision Based Method. Proceedings of the International Conference RAGUSA SHWA 2012 on \u201cSafety Health and Welfare in Agriculture and in Agro-food Systems\u201d, Ragusa, Italy."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sun, J., He, X., Ge, X., Wu, X., Shen, J., and Song, Y. (2018). Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background. Agriculture, 8.","DOI":"10.20944\/preprints201810.0524.v1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, G., Nouaze, J.C., Touko Mbouembe, P.L., and Kim, J.H. (2020). YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3. Sensors, 20.","DOI":"10.3390\/s20072145"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. Acm."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s11119-019-09642-0","article-title":"Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of \u2018MangoYOLO\u2019","volume":"20","author":"Koirala","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.34133\/2019\/1525874","article-title":"A weakly supervised deep learning framework for sorghum head detection and counting","volume":"2019","author":"Ghosal","year":"2019","journal-title":"Plant Phenomics"},{"key":"ref_34","unstructured":"Desai, S.V., Chandra, A.L., Guo, W., Ninomiya, S., and Balasubramanian, V.N. (2019). An adaptive supervision framework for active learning in object detection. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chandra, A.L., Desai, S.V., Balasubramanian, V.N., Ninomiya, S., and Guo, W. (2019). Active Learning with weak supervision for cost-effective panicle detection in cereal crops. arXiv.","DOI":"10.1186\/s13007-020-00575-8"},{"key":"ref_36","unstructured":"S\u00f8rensen, R.A., Rasmussen, J., Nielsen, J., and J\u00f8rgensen, R. (2017, January 2\u20136). Thistle Detection Using Convolutional Neural Networks. Proceedings of the 2017 EFITA WCCA Congress, Montpellier, France."},{"key":"ref_37","unstructured":"Jiang, Z., Liu, C., Hendricks, N.P., Ganapathysubramanian, B., Hayes, D.J., and Sarkar, S. (2018). Predicting County Level Corn Yields Using Deep Long Short Term Memory Models. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"You, J., Li, X., Low, M., Lobell, D., and Ermon, S. (2017, January 4\u20139). Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11172"},{"key":"ref_39","unstructured":"Shadrin, D., Pukalchik, M., Uryasheva, A., Tsykunov, E., Yashin, G., Rodichenko, N., and Tsetserukou, D. (2020). Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2984\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T09:37:29Z","timestamp":1724319449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,25]]},"references-count":39,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102984"],"URL":"https:\/\/doi.org\/10.3390\/s20102984","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,5,25]]}}}