基于机器学习的机械压实对大豆产量的影响预测研究  

Effect of Mechanical Compaction on Soybean Yield Based on Machine Learning

作　　者：周修理[1] 秦娜 王开宇 孙浩 王大维 乔金友[2] ZHOU Xiuli;QIN Na;WANG Kaiyu;SUN Hao;WANG Dawei;QIAO Jinyou(College of Electrical and Information,Northeast Agricultural University,Harbin 150030,China;College of Engineering,Northeast Agricultural University,Harbin 150030,China)

机构地区：[1]东北农业大学电气与信息学院,哈尔滨150030 [2]东北农业大学工程学院,哈尔滨150030

出　　处：《农业机械学报》2023年第11期139-147,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2021YFD2000405-2);财政部和农业农村部:国家现代农业产业技术体系项目(CARS-04-PS24)。

摘　　要：为评估农业机械作业对大豆产量的影响,本文开展不同机型、不同压实次数的拖拉机压实试验,获取不同压实环境中的土壤物理性质和大豆产量数据,分别从影响大豆产量的机械因素、土壤因素和复合因素出发,使用多元线性回归(Multiple linear regression,MLR)、随机森林(Random forest,RF)、自适应增强模型(Adaptive boosting,AdaBoost)、人工神经网络(Artificial neural network,ANN)4种机器学习算法建立大豆产量影响预测模型,对模型性能及模型特征重要性进行综合分析。研究结果表明,机械作业与大豆产量间关系复杂,集成学习算法(AdaBoost和RF)所建立的模型具有更好的拟合效果,模型决定系数更高;利用复合因素对大豆产量建立的模型拟合度最高,其次为机械因素和土壤因素,其中基于AdaBoost的复合因素对大豆产量影响模型其拟合程度最优,其R2为0.92,MAE为1.33%,RMSE为1.86%;机械因素、土壤因素都会影响大豆产量,其中机械压实次数以及表层和亚表层的土壤坚实度为影响大豆产量的重要因素,在实际生产中可通过减少机械作业次数、疏松表层及亚表层土壤来改善机械压实影响。Aiming to find a more accurate method to assess the effect of agricultural machinery compaction on soybean yield,data of soil physical properties and soybean yield in different compaction environments were obtained by carrying out different numbers of compaction walks with different types of tractors.Soybean yield forecast models were developed from mechanical factors,soil factors,and composite factors which affected soybean growth,respectively.To find out the differences of models built by different types of machine learning algorithms,multiple linear regression(MLR),random forest(RF),adaptive boosting(AdaBoost),and artificial neural network(ANN)were used in modeling.In addition,the importance of model features was comprehensively analyzed.The results showed that the relationship between mechanical operation and crop yield was complex,and the models built by integrated learning algorithms(AdaBoost and RF)had a better fit and higher coefficient of determination.Among the machine learning algorithms used,the best performance of the models built was AdaBoost,followed by random forest,artificial neural network and multiple linear regression.The model built using composite factors for soybean yield had the best fit,followed by mechanical and soil factors.The AdaBoost⁃based composite factor for soybean yield forecast model had the optimal fit with R2 of 0.92,MAE of 1.33%and RMSE of 1.86%.Mechanical factors and soil factors all had an effect on the variation of soybean yield.The number of mechanical compaction,soil penetration resistance in the surface and subsurface layers were the important factors affecting soybean yield.Therefore,the effects from mechanical compaction can be relieved by reducing the number of mechanical operation and loosening soil penetration resistance of the surface and subsurface soils.

关 键 词：机械压实 大豆产量 机器学习 土壤坚实度 预测模型 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] S233.1[自动化与计算机技术—控制科学与工程]