Optimizing Seed Selection Using Random Forest for Sustainable Agriculture: Using Weather and Soil Data
Keywords:
Machine learning, Random forest, Seed selection, Sustainable agricultureAbstract
Optimizing seed selection is essential for managing issues with unpredictable weather patterns and a variety of soil types, both of which are crucial for sustainable agriculture especially in Indonesia. Machine learning techniques, particularly ensemble methods, have gained prominence in agricultural solutions for their predictive reliability. This study introduces a web-based seed selection tool designed for Indonesian farmers, using information data from Visual Crossing Weather Data and manually inputted soil type. A predictive model was developed using the Random Forest method, a method of ensemble learning that is well-known for improving accuracy and reducing the danger of overfitting. The system achieved a notable accuracy of 87.05%, offering reliable recommendations for seed selection based on environmental and soil types. The platform’s web-based design ensures accessibility and eliminating the need for large storage capacities. This research highlights the effectiveness of integrating Random Forest with diverse datasets to address agricultural challenges. The system encourages sustainable farming methods and helps to improve agricultural productivity and resource management in an economical and user-friendly way by equipping farmers with machine learning.
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