Simulation of cotton plant height under dense planting in Aral Reclamation Area of southern Xinjiang
[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 16 000.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.
万方数据
维普
