SUSTAINABLE RICE PRODUCTION: AN ANALYTICAL APPROACH USING MACHINE LEARNING TECHNIQUES
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Abstract
Sustainable rice production is critical for ensuring food security while minimizing environmental impacts. This study employs machine learning techniques to analyze a comprehensive dataset comprising various environmental factors affecting rice yield and soil pH levels. The authors developed a regression model to predict soil pH after harvest and a classification model to identify rice varieties based on environmental conditions. The regression model achieved a Mean Squared Error of 0.90 and an R² score of 0.05, indicating limited explanatory power for pH prediction. Conversely, the classification model demonstrated high accuracy (approximately 97%) in categorizing rice varieties, underscoring the potential of machine learning in enhancing agricultural practices. The findings highlight key predictors such as humidity and rainfall, offering valuable insights for farmers and policymakers aiming to adopt sustainable practices in rice cultivation.
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