南疆阿拉尔垦区密植棉花株高模拟研究  

Simulation of cotton plant height under dense planting in Aral Reclamation Area of southern Xinjiang

作　　者：范振岐[1,2] Fan Zhenqi(College of Information Engineering,Tarim University,Aral,Xinjiang 843300,China;Key Laboratory of Tarim Oasis Agriculture,Ministry of Education/Tarim University,Aral,Xinjiang 843300,China)

机构地区：[1]塔里木大学信息工程学院,新疆阿拉尔843300 [2]塔里木绿洲农业教育部重点实验室/塔里木大学,新疆阿拉尔843300

出　　处：《棉花学报》2024年第4期320-327,共8页Cotton Science

基　　金：国家自然科学基金“新疆阿拉尔垦区天然彩色棉花高光效株型数字化构建研究”(61662064)。

摘　　要：【目的】探讨新疆阿拉尔垦区密植条件下不同模型对棉花株高的预测效果。【方法】以株型差异较大的新陆中81号和塔河2号为试验材料,在阿拉尔垦区16000株·hm^(-2)密植条件下开展大田试验,用Python语言建立株高生长的逻辑斯谛(logistic)、冈珀茨(Gompertz)、理查德(Richards)方程和决策树机器学习预测模型,并对模型的预测精度进行分析。【结果】Logistic、Gompertz和Richards模型中,新陆中81号株高的均方根误差(root mean square error,RMSE)分别为8.38%、7.49%和7.52%,平均绝对误差(mean absolute error,MAE)分别为6.80%、5.79%和5.82%;塔河2号株高的RMSE分别为6.09%、4.77%和4.85%,MAE分别为4.52%、3.34%和3.36%。决策树机器学习方法中,新陆中81号与塔河2号株高的RMSE分别为6.91%和3.27%,MAE分别为5.04%和2.16%。Logistic、Gompertz和Richards生长方程以及决策树机器学习方法均能较好地预测密植条件下棉花株高的生长,但在预测精度上决策树机器学习方法总体上优于生长方程。【结论】基于决策树的机器学习方法不需要用数理统计知识解释模型,训练模型需要的数据量也较少,模拟精度更高,在模拟棉花株高方面有一定优势,是对传统生长方程的有益补充。[Objective]This study aims to explore the prediction effects of different models on cotton plant height under high dense planting conditions in the Aral Reclamation Area,Xinjiang.[Methods]Xinluzhong 81 and Tahe 2,which are different in plant type,were used as experimental materials for field experiment under the high dense planting condition of 16000·hm^(-2) in Aral Reclamation Area.Prediction models for plant height growth were established using logistic,Gompertz,Richards growth equations,and decision tree machine learning methods using Python language.In addition,the prediction accuracy of the models was analyzed.[Results]For the logistic,Gompertz,and Richards models,the root mean square error(RMSE)of Xinluzhong 81 was 8.38%,7.49%,and 7.52%,respectively,and the mean absolute error(MAE)was 6.80%,5.79%,and 5.82%,respectively;the RMSE of Tahe 2 was 6.09%,4.77%,and 4.85%,while the MAE was 4.52%,3.34%,and 3.36%,respectively.The RMSE of Xinluzhong 81 and Tahe 2 by using decision tree machine learning method were 6.91%and 3.27%,respectively,and the MAE were 5.04%and 2.16%,respectively.The results indicated that logistic,Gompertz,and Richards growth equations and decision tree machine learning methods can effectively reflect the growth of cotton plant height under high dense planting condition.However,in terms of prediction accuracy,decision tree machine learning methods was generally superior to the three growth equations.[Conclusion]The machine learning method based on decision tree does not require mathematical and statistical knowledge to explain the model,training the model requires less data,and can achieve higher simulation accuracy.It has certain advantages in simulating cotton plant height,and is a beneficial supplement to the traditional growth equations.

关 键 词：棉花 株高 生长方程 决策树 机器学习 

分 类 号：S562[农业科学—作物学]