A study on the use of machine learning models as a predictor for crop yield.
Decision Tree Regressor, Crop Yield Prediction, Support Vector Machine, Gradient Boosting Regressor, Random Forest Regressor
Crop yield prediction has become quite significant for maintaining food security, practical resource utilization, and improving the economic stability of this particular industry. Therefore, the following research was conducted to propose a suitable machine learning model in order to forecast crop yields for 101 countries using 55 years of historical data. In the study, three major machine learning algorithms, such as Random Forest Regressor, Support Vector Machines, and Gradient Boosting Regressor, were tested on a provided dataset.
These indeed showed the highest R-squared score with the Decision Tree Regressor at 0.9606, hence explaining 96.06% of the variance in the data. Feature importance analysis revealed that crop type, pesticide use, rainfall, and temperature had been the most influential in the model's predictions.
The results of this study depicted the capabilities of machine learning techniques in addressing the challenge of crop yield forecasting. The strong predictive model can be used to give valid information to farmers and policymakers to improve decision making, resource allocation, and ultimately agricultural productivity. However, the performance of the model is constrained by data quality and exclusion of unexpected events. Additional real-time data sources and variables that will increase its adaptability and accuracy in different agricultural contexts need to be integrated into the model and pursued in future research.
"A study on the use of machine learning models as a predictor for crop yield.", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.a100-a110, October-2024, Available :https://ijnrd.org/papers/IJNRD2410016.pdf
Volume 9
Issue 10,
October-2024
Pages : a100-a110
Paper Reg. ID: IJNRD_300949
Published Paper Id: IJNRD2410016
Downloads: 000101
Research Area: Science and Technology
Country: Al Karama, dubai, United Arab Emirates
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
