首页|Reports Outline Machine Learning Study Results from Iowa State University (Devel opment of a Method for Soil Tilth Quality Evaluation from Crumbling Roller Baske ts Using Deep Machine Learning Models)
Reports Outline Machine Learning Study Results from Iowa State University (Devel opment of a Method for Soil Tilth Quality Evaluation from Crumbling Roller Baske ts Using Deep Machine Learning Models)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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).”
Iowa State UniversityAmesIowaUnite d StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Lea rning
NETL
NSTL
