Utilizing Machine Learning and Clustering Techniques for Crop Recommendation Food security worldwide heavily depends on agriculture; however, farmers often face challenges in selecting the most appropriate crops for their fields due to diverse soil characteristics, weather conditions, and precipitation levels. This research introduces a Crop Recommendation System based on Machine Learning, employing K-Means Clustering, an unsupervised learning method, to categorize areas according to temperature, rainfall, soil pH, and soil type. The system analyzed historical farming data to group similar regions and propose ideal crops for cultivation. The model was developed using a dataset comprising soil and climate information from various geographic locations. Users can access a web-based interface to input their local parameters and receive dynamic predictions for optimal crops. The findings demonstrated that clustering offers a robust solution for precision farming, enabling data-driven crop selection. This system is designed to assist farmers in making well-informed decisions, potentially leading to enhanced agricultural output and long-term sustainability.
Machine Learning, Crop Recommendation, K-Means Clustering, Precision Agriculture, Soil Analysis
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