摘要
甘蔗产量预测对于制定甘蔗生长期间的精准管理决策具有重要意义。遗传算法(Genetic algorithm,GA)优化神经网络可以提高预测效率及预测精度,通过高速计算快速找到最优解。基于湛江观测实验站2011—2020年间田间物联网获取的气象因子(大气相对湿度、大气温度、降雨量)、田间水热因子及甘蔗产量,采用BP神经网络及GA-BP神经网络模型对所选地区甘蔗产量进行预测与相关性分析。结果表明,通过Pearson及Spearman相关系数可知,甘蔗产量与月土壤最高温度、月土壤最低温度、月土壤平均温度、月大气最高温度、月大气平均温度、月大气平均相对湿度为极显著相关,相关系数高于0.7,与月土壤平均含水率、月降雨量显著相关,与月大气最低温度相关性较弱。GA-BP神经网络模型对甘蔗产量的预测精度明显高于BP神经网络模型,R^(2)达到0.8428,MAPE仅为0.90%,RMSE为1.10 t/hm^(2),预测值与试验值之间拟合程度较高,V型交叉验证结果表明模型预测结果准确稳定。因此,GA-BP模型能够更加科学、合理地预测甘蔗产量,对甘蔗田间管理措施及统筹分配具有重要的指导意义。
Sugarcane yield prediction is important for making accurate management decisions during sugarcane growth.Genetic algorithm(GA)optimized neural network can improve the prediction efficiency and prediction accuracy,and find the optimal solution quickly by high-speed calculation.Based on the meteorological factors(atmospheric humidity,atmospheric temperature,rainfall),field hydrothermal factors and sugarcane yield obtained from the field IOT at Zhanjiang Observation and Experimental Station during 2011—2020,BP neural network and GA-BP neural network models were used to predict and correlate the sugarcane yield in the selected areas.The results showed that the correlation coefficients of Pearson and Spearman showed that sugarcane yield was highly significantly correlated with monthly maximum soil temperature,monthly minimum soil temperature,monthly average soil temperature,monthly maximum atmospheric temperature,monthly average atmospheric temperature,monthly average atmospheric humidity with correlation coefficients higher than 0.7,significantly correlated with monthly average soil water content,monthly rainfall,and weakly correlated with monthly minimum atmospheric temperature.Under the GA-BP neural network model,the prediction accuracy of sugarcane yield was significantly higher than that of the BP neural network model,with R^(2)reaching 0.8428,MAPE of only 0.90%,and RMSE of 1.10 t/hm^(2).The degree of fit between the predicted and experimental values was high,and the V-cross validation results showed that the model prediction results were accurate and stable.Therefore,GA-BP prediction can predict sugarcane yield more scientifically and rationally,which was an important guiding significance for sugarcane field management measures and coordinated allocation.
作者
于珍珍
邹华芬
于德水
李海亮
孙海天
汪春
YU Zhenzhen;ZOU Huafen;YU Deshui;LI Hailiang;SUN Haitian;WANG Chun(College of Engineering,Heilongjiang Bayi Agricultural University,Daqing 163319,China;South Subtropical Crops Research Institute,Chinese Academy of Tropical Agricultural Sciences,Zhanjiang 524091,China;School of Management,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第10期277-283,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
海南省自然科学基金面上项目(322MS118)
海南省自然科学基金青年基金项目(322QN375)
关键词
甘蔗
产量预测
气象因子
田间水热因子
BP神经网络
遗传算法
sugarcane
yield prediction
meteorological factor
field water and heat factor
BP neural network
genetic algorithm
