摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-调查人员讨论人工智能的新发现。根据NewsRx记者来自爱荷华州艾姆斯的新闻报道，研究表明，"使用圆盘、抛土机和滚篮的组合耕作可以使压实的土壤松散，并使土壤块变得粗糙"。我们的新闻记者从爱荷华州立大学的研究中获得了一句话：“评价联合耕作土壤耕作质量的统计方法是有限的。光探测和测距(LiDAR)数据和机器学习模型(随机森林(RF)，支持向量机(SVM)，采用完全随机设计的(CRD)粘壤土耕作试验,采用激光雷达(Stop and Go and On-the-Go)和三维RGB视觉图像,研究了滚篮下翻、滚篮上翻、免耕三种耕作方式对土壤剖面的影响,并利用RF、SVM和SVM对土壤剖面进行了分析。LiDAR数据集上的NN方法将中位数、均值、最大值和标准差确定为受滚筒设置统计影响的重要变量的顶部特征S。应用对五个统计措施S的多变量判别分析，预测了三个土壤耕作类别，平均预测率为77%（滚筒篮下降）、64%（滚筒篮上升）和90%（无耕作）。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting originating from Ames, Iowa , by NewsRx correspondents, research stated, “A combination tillage with disks, rippers, and roller baskets allows the loosening of compacted soils and the crum bling of soil clods.” Our news correspondents obtained a quote from the research from Iowa State Unive rsity: “Statistical methods for evaluating the soil tilth quality of combination tillage are limited. Light Detection and Ranging (LiDAR) data and machine learn ing models (Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)) are proposed to investigate roller basket pressure settings on soil tilth quality. Soil profiles were measured using LiDAR (stop and go and on-the-go) an d RGB visual images from a Completely Randomized Design (CRD) tillage experiment on clay loam soil with treatments of roller basket down, roller basket up, and no-till in three replicates. Utilizing RF, SVM, and NN methods on the LiDAR data set identified median, mean, maximum, and standard deviation as the top feature s of importance variables that were statistically affected by the roller setting s. Applying multivariate discriminatory analysis on the five statistical measure s, three soil tilth classes were predicted with mean prediction rates of 77% (Roller-basket down), 64% (Roller-basket up), and 90% (No till).”
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