Crop Care Suite Using Machine Learning

Abstract

The project, “Crop Care Suite: ML-Based System for Crop Recommendation, Fertilizer Suggestion, and herbs sickness Detection,” combines machine learning, Flask framework, crop recommendation, fertilizer suggestion, and plant disease detection. Its modules analyze factors like soil type, weather conditions, historical data, crop selection, soil composition, nutrient requirements, and image processing. The system aims to optimize agricultural practices, increase crop productivity, reduce yield losses, and promote sustainable farming. It leverages ML algorithms, image processing techniques, Flask framework, personalized recommendations, timely disease detection, and preventive measures. Key keywords: machine learning, Flask, Crop Care Suite, crop recommendation, fertilizer suggestion, plant disease detection, soil type, weather conditions, historical data, crop selection, soil composition, nutrient requirements, image processing, agricultural practices, crop productivity, yield losses, sustainable farming.