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作 者:贾金豹 朱成娟 汪家全 周鹏 Jia Jinbao;Zhu Chengjuan;Wang Jiaquan;Zhou Peng(Xinyang Vocational College of Aart,Xinyang,464000,China;School of Traffic and Transportation Engineering,Dalian Jiaotong University,Dalian,116028,China;College of Information and Management Science,Henan Agricultural University,Zhengzhou,450003,China)
机构地区:[1]信阳艺术职业学院,河南信阳464000 [2]大连交通大学交通运输工程学院,大连市116028 [3]河南农业大学信息与管理科学学院,郑州市450003
出 处:《中国农机化学报》2024年第10期206-214,共9页Journal of Chinese Agricultural Mechanization
基 金:河南省高等学校重点科研项目(23B413004)。
摘 要:玉米作为河南省主要种植作物之一,作物产量预测对区域贸易和粮食安全具有重要意义。为建立简单、及时、准确的作物叶面积指数LAI和产量预测模型,采用多元线性回归MLR、偏最小二乘回归PLSR和决策树DT机器学习技术,结合玉米生理参数因子(P1)、光谱特征波段(P2)、土壤性质参数(P3)和气象参数(P4)进行多因子组合构建玉米LAI和产量的估测模型。研究结果表明,在3种机器学习方法中,籽粒形成期的LAI估测精度显著优于其他生育时期,而成熟期的产量模型估测精度显著优于其他时期;在5种多因子组合中,PLSR算法结合P1+P2+P3+P4多因子组合构建的模型达到最高精度,其中LAI估测最高为R_(v)^(2)=0.84,RMSEv=0.38,产量估测最高为R_(v)^(2)=0.79,RMSEv=982 kg/hm^(2)。为我国北方玉米种植区的玉米生长和产量预测提供技术支持和理论依据,提高预测的准确性和效率,对农业生产管理和决策制定具有重要意义。As one of the main crops in Henan Province,maize yield prediction holds significant importance for regional trade and food security.In order to establish a simple,timely,and accurate model for predicting crop LAI and yield,this study employs multiple linear regression(MLR),partial least squares regression(PLSR),and decision tree(DT)machine learning techniques.These techniques are combined with multi‑factor data,including maize physiological parameters(P1),spectral characteristic bands(P2),soil property parameters(P3),and meteorological parameters(P4),to construct estimation models for maize LAI and yield.The study results indicate that among the three machine learning methods,the LAI estimation accuracy during the grain filling stage is significantly higher than in other growth stages,while the yield estimation accuracy during the maturity stage is significantly higher than in other stages.Among the five multi‑factor combinations,the PLSR algorithm combined with the P1+P2+P3+P4 multi‑factor combination has achieved the highest accuracy,with the highest LAI estimation at Rv_(2)=0.84 and RMSEv=0.38,and the highest yield estimation at Rv 2=0.79 and RMSEv=982 kg/hm^(2).These findings provide technical support and theoretical basis for regional maize growth and yield prediction in the maize‑growing areas of northern China,enhancing prediction accuracy and efficiency,and are of great significance for agricultural production management and decision‑making.
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