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Biomedical

Yield prediction for crops by gradient-based algorithms

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Pavithra Mahesh,

Pavithra Mahesh


Rajkumar Soundrapandiyan

Rajkumar Soundrapandiyan


  Peer Reviewed

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© attribution CC-BY

  • 0

rating
471 Views

Added on

2024-10-19

Doi: http://dx.doi.org/10.1371/journal.pone.0291928

Related Subjects
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Abstract

A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R2 values were calculated to compare the predicted and observed rice yields, resulting in the following values: CatBoost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%.

Key Questions

1. What is the primary objective of the study?

The study aims to develop a framework for assessing the effectiveness of different machine learning algorithms in predicting crop yields, considering factors such as temperature, soil fertility, water availability, and seasonal variations.

2. What methodology was employed in the research?

The research evaluates the performance of three gradient-based machine learning algorithms: Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost). These models were trained using data on pesticides, rainfall, and average temperature to predict crop yields.

3. What were the main findings of the study?

The study found that CatBoost, LightGBM, and XGBoost outperformed other algorithms in predicting crop yields, as indicated by their lower Root Mean Squared Error (RMSE) and higher R² values.

Summary

Pavithra Mahesh and Rajkumar Soundrapandiyan (2024) developed a framework to evaluate machine learning algorithms for forecasting crop yields. The study demonstrated that CatBoost, LightGBM, and XGBoost are effective in predicting crop yields, considering environmental and economic factors.

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ARTICLE USAGE


Article usage: Oct-2024 to May-2025
Show by month Manuscript Video Summary
2025 May 49 49
2025 April 80 80
2025 March 64 64
2025 February 52 52
2025 January 100 100
2024 December 47 47
2024 November 56 56
2024 October 23 23
Total 471 471
Show by month Manuscript Video Summary
2025 May 49 49
2025 April 80 80
2025 March 64 64
2025 February 52 52
2025 January 100 100
2024 December 47 47
2024 November 56 56
2024 October 23 23
Total 471 471
Related Subjects
Anatomy
Biochemistry
Epidemiology
Genetics
Neuroscience
Psychology
Oncology
Medicine
Musculoskeletal science
Pediatrics
Pathology
Pharmacology
Physiology
Psychiatry
Primary care
Women and reproductive health
copyright icon

© attribution CC-BY

  • 0

rating
471 Views

Added on

2024-10-19

Doi: http://dx.doi.org/10.1371/journal.pone.0291928

Related Subjects
Anatomy
Biochemistry
Epidemiology
Genetics
Neuroscience
Psychology
Oncology
Medicine
Musculoskeletal science
Pediatrics
Pathology
Pharmacology
Physiology
Psychiatry
Primary care
Women and reproductive health

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