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Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction

文献类型： 外文期刊

作者： Lv, Xinyue 1 ; Li, Youli 2 ; Zhangzhong, Lili 2 ; Tong, Chaoyang 2 ; Wei, Yibo 1 ; Li, Guangwei 2 ; Yang, Yingru 4 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

3.Ningxia Univ, Yinchuan 750021, Ningxia, Peoples R China

4.Shijiazhuang Acad Agr & Forestry Sci, Shijiazhuang 050047, Hebei, Peoples R China

关键词： Multi-source data; Image segmentation; Machine learning; Water requirement prediction

期刊名称：SCIENTIFIC REPORTS （ 影响因子：3.9； 五年影响因子：4.3 ）

ISSN： 2045-2322

年卷期： 2025 年 15 卷 1 期

页码：

收录情况： SCI

摘要： Accurate scientific predicting of water requirements for protected agriculture crops is essential for informed irrigation management. The Penman-Monteith model, endorsed by the Food and Agriculture Organization of the United Nations (FAO), is currently the predominant approach for estimating crop water needs. However, the complexity of its numerous parameters and the potential for empirical parameter inaccuracies pose significant challenges to precise water requirement predictions. In this study, we introduce a novel water demand prediction model for greenhouse tomato crops that leverages multi-source data fusion. We employed the ExG (Excess Green) algorithm and the maximum inter-class variance method to develop an algorithm for extracting canopy coverage from image segmentation. Subsequently, Spearman correlation analysis was utilized to select the combination of canopy coverage and environmental data, followed by the random forest feature importance ranking method to identify the most optimal feature variables. We constructed average fusion, weighted fusion, and stacking fusion models based on RandomForest, LightGBM, and CatBoost machine learning algorithms to accurately predict the water requirements of greenhouse tomato crops. The results show that the stacking model has the best prediction effect, and the error is lower than that of RandomForest, LightGBM, CatBoost, Average fusion model and Weighted fusion model. The feature combination of Tmax, Ts, and CC, filtered using Spearman and RandomForest, demonstrated the lowest prediction errors, with reductions in MSE, MAE, and RMSE of over 4%, 14%, and 3%, respectively, compared to other parameter combinations. The R2 value increased by 1%, indicating enhanced reliability and generalization. This research comprehensively considered various factors, including environmental, soil, and crop growth conditions, that influence crop water requirements. By integrating image and environmental data, we developed a water requirement prediction model for greenhouse tomato crops based on the principles of decoupling and minimizing characteristic parameters, offering innovative technical support for scientific irrigation practices.

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