Ramie Yield SSA-BP Prediction Model Based on Climate Variables
The yield of ramie has a high correlation with the climate factors during the growth period,and the final yield can be effectively and accurately predicted by constructing a ramie yield prediction model based on climate variables.The BP neural network has strong data analysis capabilities and is widely used in crop yield prediction modeling.However,traditional BP neural networks have problems such as low accuracy and poor robustness.The sparrow search algorithm(SSA)can be used to optimize the BP neural network model.Based on the fiber yield,fresh skin yield and climate data collected in ramie long-term positioning experiment from 2010 to 2019,this study analyzed the changing trend of climate factors in 10 years and their impacts on the perennial ramie yield by comparing the performance of BP neural network model and the optimized SSA-BP neural network model in predicting ramie yield to determine the best prediction model.It showed that there were extremely significant correlations between the yield of ramie and 4 meteorological factors including the seasonal average temperature,the average seasonal extreme maximum temperature,the seasonal extreme minimum temperature,and the seasonal average sunshine hours,among which the ramie yield had the highest correlation with the seasonal average temperature.SSA algorithm could effectively optimize the BP neural network.R2 of the ramie fiber yield prediction model and fresh skin yield prediction model based on SSA-BP were 0.591 3 and 0.679 1,respectively,which were higher than that of the ramie fiber yield prediction model(R2= 0.405 7)and fresh skin yield prediction model(R2=0.551 8).Therefore,the SSA-BP model could predict ramie yield more scientifically and reasonably,which was of great guiding significance for field management and overall plan of ramie production.
国家科技期刊平台
NSTL
万方数据
维普
