Guifang Li

1 Jiande Qingrun Modern Agriculture Development Co., Ltd., Jiande 311600, Zhejiang, China
2 Zhejiang Agronomist College, Hangzhou 310021, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2026, Vol. 16, No. 3   
Received: 24 Mar., 2026    Accepted: 28 Apr., 2026    Published: 12 May, 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.

With the intensification of climate change and the continuous transformation of agricultural production methods, the extent to which eggplant yields are jointly influenced by fertilizer management and climatic conditions has become increasingly evident. Focusing on fertilization factors and climatic variables as the core subjects of inquiry, this study systematically analyzes the mechanisms by which temperature, precipitation, humidity, and fertilizer inputs affect eggplant yield formation, while also exploring the interactive effects between climate and fertilization. To this end, regional meteorological data, soil nutrient data, and field yield data were collected to construct an eggplant yield prediction model based on a combination of statistical analysis and machine learning techniques. The research focuses on variable selection, feature engineering, model training, and the optimization of predictive performance, while also comparing the differences in predictive accuracy and stability between regression models and machine learning algorithms. The results indicate that temperature fluctuations, soil moisture conditions, and nitrogen fertilizer inputs are critical factors influencing eggplant yields, and that the coupled effects of these multiple factors can significantly enhance the accuracy of the prediction model. A case study further validates the model's applicability within regional agricultural production contexts, providing a scientific basis for precision fertilization management, agricultural risk assessment, and smart farming decision-making. This study holds significant theoretical and practical implications for improving eggplant production efficiency, optimizing resource utilization, and fostering sustainable agricultural development.

Eggplant yield prediction; Fertilization management; Climate variables; Machine learning; Precision agriculture