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Machine learning-based comparative analysis of weather-driven rice and sugarcane yield forecasting models

Indian Agricultural Research Journals

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Title Machine learning-based comparative analysis of weather-driven rice and sugarcane yield forecasting models
 
Creator V. B. Virani
Neeraj Kumar
D. S. Rathod
D. P. Mobh
 
Subject Machine learning
Yield forecasting
Random Forest
Booster
Sugarcane
Rice
 
Description This study investigates the use of various machine learning algorithms for predicting rice and sugarcane yields for Navsari district of Gujarat, India. Recognizing the critical role of weather in crop productivity, accurate forecasting becomes essential for effective resource management. In methodology, weekly averages and weighted weather indices were computed based on daily weather data to develop forecast models using machine learning algorithms such as Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), XGBoost (XGB), Gradient Boost Regression (GBR), and Decision Tree (DT). Results show that RF and GBR algorithms outperform others in rice yield forecasting, while Gradient Booster and XGBoost demonstrate high accuracy in sugarcane yield prediction. However, the Mean Absolute Percentage Error (MAPE) values remained above 8%, indicating room for improvement. The study also emphasizes the importance of tuning hyperparameters for each machine learning algorithms (MLA) to achieve the most accurate predictions. Overall, the findings contribute valuable insights for stakeholders, including agricultural planners, policymakers, and researchers, emphasizing the need for continued refinement and validation of models to optimize agricultural planning and decision-making in this region. MLA highlight that features associated with temperature and relative humidity (RH) play a crucial role as the most significant contributors to the forecasting models for both rice and sugarcane yield. Introducing additional features, particularly remote sensing data, holds the potential to decrease the current error range of 8 to 10% to a more favourable and lower value. 
 
Publisher Association of Rice Research Workers
 
Date 2024-06-04
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://epubs.icar.org.in/index.php/OIJR/article/view/147590
 
Source ORYZA-An International Journal of Rice; Vol. 61 No. 2 (2024): April-June; 150-159
2249-5266
0474-7615
 
Language eng
 
Relation https://epubs.icar.org.in/index.php/OIJR/article/view/147590/54717
 
Rights Copyright (c) 2024 Association of Rice Research Workers
http://creativecommons.org/licenses/by/4.0