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Harvest Horizon: Machine Learning Advancements in Crop Projection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 17 |
| Year of Publication: 2024 |
| Authors: Samuel Benny Varghese, Eldho K.J. |
10.5120/ijca2024923553
|
Samuel Benny Varghese, Eldho K.J. . Harvest Horizon: Machine Learning Advancements in Crop Projection. International Journal of Computer Applications. 186, 17 ( Apr 2024), 1-6. DOI=10.5120/ijca2024923553
Abstract
This study looks into agricultural yield prediction using the Random Forest machine learning method. Through the incorporation of several environmental factors, including soil properties, climatic data, and past crop yields, the model is trained to precisely predict crop production in the future. Through meticulous cross-validation processes, the study evaluates the Random Forest algorithm’s performance and contrasts it with conventional statistical methods. Results show that Random Forest is effective in forecasting crop yields, underscoring its potential to help stakeholders, regulators, and farmers make wise agricultural decisions. This research advances agricultural productivity and resilience in the face of changing climatic conditions by utilizing machine learning, which supports sustainable food production methods. This study explores the use of machine learning technique Random Forest to forecast crop output. The model is trained with extensive environmental data analysis, including soil quality and climate data, to produce precise projections. The findings demonstrate the potential of Random Forest to support sustainable farming practices by assisting in decision-making.
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