Soil Bench: A benchmark of seven machine learning algorithms to predict crop yield using soil data of Aus, Aman and Boro

Soil Bench is a benchmark of seven algorithms - Linear Regression, Lasso Regression, Ridge Regression, KNN (K-Nearest Neighbors), SVR (Support Vector Regression), Elastic Net and Random Forest - on soil data of three types of crops - Aus , Aman and Boro - using fourty four years of Bangladesh specific data.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-02
DOI
https://doi.org/10.5281/zenodo.21112419
Primary Topic
Agricultural Economics and Practices
Type
preprint

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Soil Bench: A benchmark of seven machine learning algorithms to predict crop yield using soil data of Aus, Aman and Boro

Asif Kazi, Andelib Mamun Kazi
Zenodo (CERN European Organization for Nuclear Research)
Agricultural Economics and Practices
preprint

Soil Bench: A benchmark of seven machine learning algorithms to predict crop yield using soil data of Aus, Aman and Boro

Asif Kazi, Andelib Mamun Kazi
preprint en

Abstract

Soil Bench is a benchmark of seven algorithms - Linear Regression, Lasso Regression, Ridge Regression, KNN (K-Nearest Neighbors), SVR (Support Vector Regression), Elastic Net and Random Forest - on soil data of three types of crops - Aus , Aman and Boro - using fourty four years of Bangladesh specific data.

Zenodo (CERN European Organization for Nuclear Research)
University of Surrey (GB), BRAC University (BD)
European Commission
Agricultural Economics and Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.