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基于智能算法的云南甘蔗产量预测

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  • NETL
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构建基于智能算法的甘蔗产量预测模型,对云南省8个甘蔗产区甘蔗产量进行预测.选取云南省临沧市、德宏傣族景颇族自治州、普洱市、文山壮族苗族自治州、红河哈尼族彝族自治州、保山市、西双版纳傣族自治州、玉溪市2000-2020年每日的气象、土壤数据及产量数据,通过专家打分法初步筛选对甘蔗产量影响较大的气象、土壤因子,应用逐步回归分析算法筛选甘蔗生长周期内的气候、土壤关键影响因子.在数据集划分和筛选关键影响因子的基础上,以每年气象、土壤因子作为输入变量,以每年甘蔗产量为输出变量,建立了BP神经网络产量预测模型.以每日和每年的气象、土壤因子作为输入向量,以甘蔗产量为输出变量,建立了长短期记忆网络(LSTM)神经网络产量预测模型.测试集结果表明,BP神经网络模型决定系数(R2)为0.916、平均绝对误差(MAE)为28.65万t、均方根误差(RMSE)为40.83万t,LSTM神经网络模型R2为0.978、MAE为16.04万t、RMSE为20.72万t.LSTM神经网络模型预测精度高,模型性能优良,能较好地预测云南省甘蔗产量.
Yunnan sugarcane yield prediction based on intelligent algorithm
A sugarcane yield prediction model based on intelligent algorithm was constructed to predict sugarcane yield in eight sugar-cane production areas in Yunnan Province.Daily meteorological and soil data and yield data of Lincang,Dehong,Pu'er,Wenshan,Honghe,Baoshan,Xishuangbanna,and Yuxi of Yunnan Province for the period of 2000 to 2020 were selected,and the meteorologi-cal and soil factors that had a greater impact on the yield of sugarcane were preliminarily screened by the expert scoring method.Step-wise regression analysis algorithm was applied to screen the key influence factors of climate and soil during the growth cycle of sugar-cane.Based on the division of the data set and the screening of the key influencing factors,a BP neural network yield prediction model was established with the annual meteorological and soil factors as the input variables and the annual sugarcane yield as the output vari-able.A Long Short-Term Memory(LSTM)neural network yield prediction model was developed using daily and annual meteorological and soil factors as input vectors and sugarcane yield as the output variable.The results of the test set showed that the coefficient of de-termination(R2)of the BP neural network model was 0.916,the mean absolute error(MAE)was 286 500 tons,and the root mean square error(RMSE)was 408 300 tons,and the R2 of the LSTM neural network model was 0.978,the MAE was 160 400 tons,and the RMSE was 207 200 tons.The prediction accuracy of the LSTM neural network model was high,and the model performance was excel-lent and could better predict the sugarcane yield in Yunnan.

intelligent algorithmsugarcaneBP neural networklong and short term memory network(LSTM)neural networkyield predictionYunnan Province

王泳智、田鹏、李富生、孙吉红、孙陈、刘振洋、刘念、钱晔

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云南农业大学,大数据学院(信息工程学院),昆明 650201

云南农业大学,农学与生物技术学院,昆明 650201

云南农业大学,云南省作物生产与智慧农业重点实验室,昆明 650201

云南农业大学,园林园艺学院,昆明 650201

云南农业大学,云南省农业大数据工程技术研究中心,昆明 650201

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智能算法 甘蔗 BP神经网络 长短期记忆网络(LSTM)神经网络 产量预测 云南省

云南省作物生产与智慧农业重点实验室开放基金项目云南主要粮经作物全智慧产业链关键技术研究与示范项目

2021ZHNY02202202AE090021

2024

10.14088/j.cnki.issn0439-8114.2024.08.022

湖北农业科学
湖北省农业科学院　华中农业大学　长江大学　黄冈师范学院

湖北农业科学

CSTPCD
影响因子：0.442
ISSN：0439-8114
年,卷(期)：2024.63(8)
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