Soybean yield prediction using machine learning algorithms under a cover crop management system

被引：4

作者：

Santos, Leticia Bernabe ^{[1
]}

Gentry, Donna ^{[4
]}

Tryforos, Alex ^{[3
]}

Fultz, Lisa ^{[2
]}

Beasley, Jeffrey ^{[1
]}

Gentimis, Thanos ^{[3
]}

机构：

[1] Louisiana State Univ, Sch Plant Environm & Soil Sci, 137 Miller Hall, Baton Rouge, LA 70803 USA

[2] Louisiana State Univ, Sch Plant Environm & Soil Sci, 310 Sturgis Hall, Baton Rouge, LA 70803 USA

[3] Louisiana State Univ, Expt Stat, 161 Martin Woodin Hall, Baton Rouge, LA 70803 USA

[4] Louisiana State Univ, Agr Ctr, 4419 Idlewild Rd, Clinton, LA 70722 USA

来源：

SMART AGRICULTURAL TECHNOLOGY | 2024年 / 8卷

关键词：

Random forests; Machine learning; Datapoint threshold; Soybeans; Yield prediction; Cover crops; Hyperparameter optimization; Hyperparameter tuning; Dataset Enhancement; PERFORMANCE; COPULA; MODELS;

D O I：

10.1016/j.atech.2024.100442

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

This research explores the predictive capabilities of random forests algorithm on datasets coming from standard experiments on crop management systems in soybeans. This is a secondary analysis of a dataset from a project evaluating the relationship of cover crop systems to soybean yield prediction. The purpose of this paper is to compare a random forest algorithm to standard statistical techniques such as linear regression on a clean information rich agronomic experiment. The main findings include an estimate of the hyperparameters for optimal predictions using random forests, a threshold for data for optimal results and a general description of comparison methodologies for AI based techniques.

引用

页数：8

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