Jili He

Zhuji Puxi Crops Professional Cooperative, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 5   
Received: 13 Aug., 2026    Accepted: 19 Sep., 2026    Published: 30 Sep., 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.

Grape growth and fruit quality are jointly influenced by environmental conditions, soil properties, and orchard management practices, making it essential to identify and quantify the relative contributions of these factors. This paper systematically analyzes the major factors affecting grape growth, yield, and fruit quality from a statistical perspective. Key indicators, including vegetative growth, yield components, soluble solids, titratable acidity, sugar-acid ratio, fruit coloration, and physiological traits, are considered. Environmental factors such as temperature, light, water availability, humidity, and soil physicochemical properties are evaluated alongside fertilization, irrigation, pruning, crop load regulation, and cluster management. Statistical approaches, including correlation analysis, analysis of variance, multiple regression, multivariate analysis, and machine learning, are discussed for identifying key influencing factors and characterizing nonlinear relationships and interactions. A case study based on orchard management data further demonstrates the application of statistical models in evaluating grape yield and quality responses. The integrated analysis provides a quantitative basis for identifying critical management factors and developing data-driven precision orchard management strategies. These findings highlight the potential of statistical analysis and intelligent technologies to improve resource-use efficiency, stabilize yield, enhance fruit quality, and promote sustainable grape production.

Grape growth; Fruit quality; Orchard management; Statistical analysis; Precision agriculture