Using a Real-Time Photosynthesis and Transpiration Monitoring System to Develop Random Forests Models for Predicting Cherry Tomato Yield in a Commercial Greenhouse-Reference-Cited by-同舟云学术

Using a Real-Time Photosynthesis and Transpiration Monitoring System to Develop Random Forests Models for Predicting Cherry Tomato Yield in a Commercial Greenhouse

Published:2024-04-01 Issue:2 Volume:62 Page:29-39
ISSN:1880-554X
Container-title:Environment Control in Biology
language:en
Short-container-title:Environ. Control Biol.

Author:

FUJIUCHI Naomichi¹,INABA Kazue¹²,OH Shinchu¹,OKAJIMA Sayaka³,ASAI Yuichiro³,NISHINA Hiroshige¹,TAKAYAMA Kotaro¹⁴

Affiliation:

1. Department of Food Production Science, Graduate School of Agriculture, Ehime University

2. PLANT DATA Co., Ltd.

3. Asai Nursery Inc.,

4. Department of Mechanical Engineering, Graduate School of Engineering, Toyohashi University of Technology

Publisher

Japanese Society of Agricultural, Biological and Environmental Engineers and Scientists

Link

https://www.jstage.jst.go.jp/article/ecb/62/2/62_29/_pdf

Reference22 articles.

1. Alhnaity, B., Pearson, S., Leontidis, G., Kollias, S.　2020.　Using deep learning to predict plant growth and yield in greenhouse environments. Acta Hortic. 1296: 425―431.

2. De Alwis, S., Zhang, Y., Na, M., Li, G.　2019.　Duo attention with deep learning on tomato yield prediction and factor interpretation. In “PRICAI 2019: Trends in Artificial Intelligence” (ed. by Nayak, A., Sharma, A.). Springer, Cham, p 704―715.

3. Breiman, L.　2001.　Random forests. Mach. Learn. 45: 5―32.

4. Dharani, M. K., Thamilselvan, R., Natesan, P., Kalaivaani, P. C. D., Santhoshkumar, S.　2021.　Review on crop prediction using deep learning techniques. J. Phys. Conf. Ser. 1767: 012026.

5. Dietterich, T. G.　2000.　Ensemble methods in machine learning. In “Multiple Classifier Systems”. Springer Berlin Heidelberg, Berlin, p 1―15.

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