Xingzhu Feng

Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 2   
Received: 22 Feb., 2026    Accepted: 30 Mar., 2026    Published: 15 Apr., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Temperature is one of the most critical environmental factors affecting the growth, development, and productivity of greenhouse tomatoes. This paper systematically reviews and analyzes the relationship between temperature dynamics and tomato yield formation under protected cultivation conditions. The study summarizes the physiological mechanisms through which temperature regulates photosynthesis, respiration, flowering, fruit set, and stress responses during different growth stages. In addition, the characteristics of greenhouse microclimates and the interactions between temperature, humidity, light, and CO₂ are discussed. Various modeling approaches, including statistical regression models, process-based crop models, and machine learning algorithms, are evaluated for their ability to predict tomato yield under variable temperature conditions. The paper also examines methods for model calibration, validation, and performance assessment using multi-season datasets. Several case studies are presented to demonstrate the practical applications of temperature-yield models in greenhouse management and precision agriculture. Finally, the challenges, limitations, and future prospects of intelligent temperature regulation and climate-adaptive modeling are highlighted to support sustainable greenhouse tomato production.

Greenhouse tomato; Temperature dynamics; Yield prediction; Crop growth model; Precision agriculture