A comparative study of machine learning models for predicting Aman rice yields in Bangladesh.

Taufiqul Islam, Tanmoy Mazumder, Md Nishad Shahriair Roni, Md Sadmin Nur

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In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Year-independent prediction of rice grain protein content using machine learning with agronomy-aligned multi-year field data.Frontiers in plant science · 2026
Article

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5 · Who and what money

Authors and funding

4 authors.

Taufiqul IslamDepartment of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.

Tanmoy MazumderDepartment of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.

Md Nishad Shahriair RoniDepartment of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.

Md Sadmin NurDepartment of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aman rice, a major staple crop in Bangladesh, is cultivated during the monsoon season and is highly dependent on climatic conditions such as rainfall and temperature. This study aims to identify the most effective machine learning models for predicting Aman rice yields by leveraging 52 years of historical data (1970-2022). Data preprocessing included outlier correction, statistical imputation, and aggregation of monthly averages for varibales like rainfall, temperature, humidity and others during the monsoon (June-September). Various machine learning models - Random Forest, Neural Network, Decision Tree, Linear Regression, and Gradient Boosting - were employed to capture yield trends under changing climatic conditions. Each model was evaluated based on Root Mean Squared Error (RMSE), R-squared (R

Indexed as

Agricultural forecastingClimate changeData-driven insightsFood securityRandom forest modelSustainable development goals (SDGs)

Identifiers

PMID39691189

DOI10.1016/j.heliyon.2024.e40764

PMCPMC11650275

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.