[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122193-en":3,"doc-seo-122193-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122193,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Fruit size prediction of tomato cultivars using machine learning algorithms","Early fruit size prediction in greenhouse tomato is crucial for growers and supply-chain stakeholders because fruit grade and yield are strongly influenced by weather conditions, crop load, and stress responses. The study develops a machine-learning approach to predict tomato fruit size at harvest using time-series fruit diameter, cumulative temperature after anthesis, and measured average temperature during prediction windows. Three models—Ridge Regression, Extra Tree Regression, and CatBoost Regression—are compared with PyCaret across three cultivars.","TYPE Original Research PUBLISHED 29 January 2025 DOI 10.3389/fpls.2025.1516255  \nOPEN ACCESS  \nEDITED BY  \nWenyu Zhang,  \nJiangsu Academy of Agricultural Sciences Wuxi Branch, China  \nREVIEWED BY  \nLiying Chang,  \nShanghai Jiao Tong University, China Jiayi Zhang,  \nJiangsu Academy of Agricultural Sciences Wuxi Branch (JAASWB), China  \n*CORRESPONDENCE  \nMasaaki Takahashi  \n [takahashim088@affrc.go.jp](takahashim088@affrc.go.jp)  \nRECEIVED 24 October 2024  \nACCEPTED 13 January 2025  \nPUBLISHED 29 January 2025  \nCITATION  \nTakahashi M, Kawasaki Y, Naito H, Lee U and Yoshi K (2025) Fruit size prediction of tomato cultivars using machine learning algorithms. Front. Plant Sci. 16:1516255 .  \ndoi: 10.3389/fpls.2025.1516255  \nCOPYRIGHT  \n© 2025 Takahashi, Kawasaki, Naito, Lee and Yoshi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nFruit size prediction of tomato cultivars using machine learning algorithms  \nMasaaki Takahashi 1*, Yasushi Kawasaki 1, Hiroki Naito 1,2, Unseok Lee 1 and Koichi Yoshi 1  \n1 Research Center for Agricultural Robotics, National Agricultural and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan, 2Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan  \nEarly fruit size prediction in greenhouse tomato (Solanum lycopersicum L.) is crucial for growers managing cultivars to reduce the yield ratio of small-sized fruit and for stakeholders in the horticultural supply chain. We aimed to develop a method for early prediction of tomato fruit size at harvest with machine learning algorithm, and three machine learning models (Ridge Regression, Extra Tree Regrreion, CatBoost Regression) were compared using the PyCaret package for Python. For constructing the models, the fruit weight estimated from the fruit diameter obtained over time for each cumulative temperature after anthesis was used as explanatory variable and the fruit weight at harvest was used as objective variable. Datasets for two different prediction periods after anthesis of three tomato cultivars (“CF Momotaro York,” “Zayda,” and “Adventure.”) were used to develop tomato size prediction models, and their performance was evaluated. We also aimed to improve the model adding the average temperature during the prediction period as an explanatory variable. When the estimated fruit size data at cumulative temperatures of 200°C d, 300°C d, and 500°C d after anthesis were used as explanatory variables, the mean absolute percentage error (MAPE) was lowest for “Zayda,” a cultivar with stable fruit diameter, at 9 . 8% for Ridge Regression. When the estimated fruit size at cumulative temperatures of 300°Cd, 500°C d, and 800°C d after anthesis were used as explanatory variables for Ridge Regression, the MAPE decreased for all cultivars: 10 . 1% for “CF Momotaro York,” 8. 8% for “Zayda,” and 10 . 0% for “Adventure.” In addition, incorporating the average temperature during the fruit size prediction period as an explanatory variable slightly increased model performance. These results indicate that this method could effectively predict tomato size at harvest in three cultivars. If fruit diameter data acquisition could be automated or simplified, it would assist in cultivation management, such as tomato thinning.  \nKEYWORDS  \nsize prediction, fruit grade, machine learning, diameter, tomato  \nFrontiers in Plant Science 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nFruit size and yield are crucial crop management considerations for horticultural fruit growers. These factors can vary based on weather conditions (Lötze and Berg","cbCaifu8aaKIbUFc","https://ap.wps.com/l/cbCaifu8aaKIbUFc","pdf",2191030,1,9,"English","en",105,"# Introduction\n## Importance of fruit size and yield\n## Existing approaches and limitations","[{\"question\":\"Why is early prediction of tomato fruit size important?\",\"answer\":\"Early prediction helps growers manage cultivars to reduce the yield ratio of small-sized fruits and improves decisions for stakeholders in the horticultural supply chain.\"},{\"question\":\"What data and variables are used to build the machine learning models?\",\"answer\":\"The models use estimated fruit weight derived from fruit diameter measured over time, using cumulative temperature after anthesis as the main explanatory variable, with harvest fruit weight as the objective.\"},{\"question\":\"How does including average temperature affect prediction performance?\",\"answer\":\"Incorporating the average temperature during the prediction period slightly increases model performance compared with using cumulative-temperature-based features alone.\"}]","Fruit size prediction of tomato cultivars using machine learning algorithms | 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is early prediction of tomato fruit size important?","Question",{"text":75,"@type":76},"Early prediction helps growers manage cultivars to reduce the yield ratio of small-sized fruits and improves decisions for stakeholders in the horticultural supply chain.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and variables are used to build the machine learning models?",{"text":80,"@type":76},"The models use estimated fruit weight derived from fruit diameter measured over time, using cumulative temperature after anthesis as the main explanatory variable, with harvest fruit weight as the objective.",{"name":82,"@type":73,"acceptedAnswer":83},"How does including average temperature affect prediction performance?",{"text":84,"@type":76},"Incorporating the average temperature during the prediction period slightly increases model performance compared with using cumulative-temperature-based features 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