Weijie Shi^1,2

1 Tongxiang Fengming Weijie Family Farm,Tongxiang, 314500, Zhejiang, China
2 Zhejiang Agronomist College, Hangzhou, 310021, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 4   
Received: 30 Jun., 2026    Accepted: 08 Aug., 2026    Published: 31 Jul., 2003

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.

Water management is a critical factor affecting melon (Cucumis melo L.) growth, yield formation, and fruit quality, particularly under conditions of increasing water scarcity and climate variability. However, the complex interactions between irrigation practices, environmental conditions, plant physiological responses, and fruit quality development remain insufficiently understood. This review focuses on the application of data-based approaches to analyze the effects of water management on melon production. The physiological mechanisms underlying water regulation of melon growth are first summarized, including the impacts of irrigation regimes on root development, photosynthesis, reproductive processes, fruit expansion, and quality formation. Subsequently, methods for collecting and integrating multi-source datasets, including irrigation information, soil moisture dynamics, climatic variables, plant growth parameters, and fruit quality indicators, are discussed. Statistical models and machine learning approaches, such as regression analysis, Random Forest, Support Vector Regression, and deep learning algorithms, are evaluated for their ability to quantify water-growth-quality relationships and predict crop responses. A case study framework is presented to demonstrate the evaluation of different irrigation strategies and the development of precision water management decision-support systems. Furthermore, the integration of IoT sensors, remote sensing, and artificial intelligence technologies is explored to improve irrigation efficiency and optimize fruit quality. Future research should focus on multi-dimensional data integration, explainable artificial intelligence, and region-specific modeling to achieve sustainable and intelligent melon production.

Melon; Water management; Fruit quality; Machine learning; Precision irrigation