ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

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.