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Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model.

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Title: Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model.
Authors: Abd El-Baki MS; Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, Egypt., Ibrahim MM; Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, Egypt., Elsayed S; Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya, Egypt., Elbeltagi A; Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, Egypt., Salem A; Civil Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt.; Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pecs, Pecs, Hungary., Abd El-Fattah NG; Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, Egypt.
Source: PloS one [PLoS One] 2026 Apr 30; Vol. 21 (4), pp. e0346503. Date of Electronic Publication: 2026 Apr 30 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
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