基于LightGBM的温室番茄冠层CWSI预测模型研究

CWSI Prediction Model of Greenhouse Tomato Canopy Based on LightGBM Algorithm

为研究温室内番茄冠层作物水分胁迫指数(CWSI)问题,通过布设多参数传感器,实时获取温室内外各环境参数。利用灰度关联分析,计算各环境参数与番茄冠层CWSI的关联度,根据关联度对环境参数进行排序,同时考虑对模型精度的影响,最终从9个环境参数中选取7个作为模型输入,建立基于LightGBM的温室番茄冠层CWSI预测模型。结合贝叶斯算法优化其中的关键参数,将模型预测结果与通过Jones经验公式计算出的CWSI做相关性分析,在相同的运算环境下,分别与GBRT和SVR模型对比。试验结果表明,基于贝叶斯优化LightGBM模型的决定系数(R^(2))、平均绝对误差(MAE)、均方根误差(RMSE)和运算时间分别为0.9601、0.0218、0.0314和0.0518 s,与GBRT和SVR模型相比,其R^(2)分别提高2.14%和14.05%,MAE分别降低0.0093和0.0612,RMSE分别降低0.0097和0.0591,时间分别缩短0.0459 s和0.0612 s。表明本研究提出的LightGBM模型性能更有效地提高了温室番茄冠层CWSI的预测精度,为实现温室番茄按需灌溉提供了参考。

In order to study the prediction of crop water stress index(CWSI)of tomato canopy in greenhouse,through the deployment of multi parameter sensors,the environmental parameter inside and outside the greenhouse can be obtained in real time.Using gray correlation analysis,the correlation degree between environmental parameters and tomato canopy CWSI and the sub factor correlation coefficient between environmental parameters was calculated,the environmental parameters were sorted according to the correlation degree,and the impact on the accuracy of the model was considered.Finally,a total of seven parameters from nine environmental parameters were selected as the model input,and a prediction model of greenhouse tomato canopy crop water stress index(CWSI)based on LightGBM was established.Combined with Bayesian algorithm to optimize the key parameters,the correlation between the prediction results of the model and the CWSI value calculated by Jones empirical formula was analyzed.Under the same computing environment,it was compared with GBRT and SVR models respectively.The experimental results showed that the coefficient of determination(R^(2)),mean absolute error(MAE),root mean square error(RMSE)and operation time of the Bayesian optimized LightGBM model were 0.9601,0.0218,0.0314 and 0.0518 s,respectively.Compared with GBRT and SVR models,R^(2)was increased by 2.14%and 14.05%respectively,MAE was reduced by 0.0093 and 0.0612 respectively,RMSE was reduced by 0.0097 and 0.0591 respectively,and the time was shortened by 0.0459 s and 0.0612 s respectively.It was showed that the LightGBM model proposed had better performance,which could effectively improve the prediction accuracy of greenhouse tomato canopy CWSI,and provide a strategy for realizing greenhouse tomato on-demand irrigation and a reference for water requirement research.