ML Based Agricultural Tool Recommendation System for Precision Farming
Ramasamy Madhumathi, K Sudharsan, R Vijayanandh, Manish Kumar · 2024
Precision farming has gained significant attention in recent years due to its potential to enhance agricultural productivity while minimizing resource usage. In this study, we propose an Agricultural Tool Recommendation System aimed at optimizing farming practices. The system leverages advanced technologies such as Machine Learning (ML) and data analytics to analyze crop-specific requirements and recommend appropriate agricultural tools. By considering factors such as soil composition, climate conditions, and crop characteristics, the system provides tailored recommendations to farmers, ultimately improving efficiency and yield. A model has been developed to evaluate the performance of several deep learning models, including Random Forest Classifier (RFC), Multi-Output Classifier and Transformer, in order to recommend the proper agricultural tools based on the crop types. Our results demonstrate that hyperparameter optimization enhances the efficacy of the Transformer and Machine Learning models, showcasing their superiority in providing accurate and customized tool recommendations for precision farming applications.