Smart Crop Recommendation for Modern Farming using Machine Learning

The agricultural sector plays a key role in global food security and livelihoods, emphasizing the importance of improving agricultural practices. In this research, we present a crop recommendation system (CRS) designed to help farmers optimize the crop selection process. CRS uses machine learning algorithms that incorporate a diverse dataset containing soil properties, climate conditions, and historical crop yield data.Our methodology includes a multi-stage approach. Initially, we conducted comprehensive data collection from various sources, including government databases and research publications. Subsequently, data pre-processing techniques are applied to clean, normalize, and integrate different data sets into a uniform format suitable for analysis.whereas we receive basic attributes like soil pH, moisture content, temperature, and rainfall levels, structural engineering is crucial. We provide an ensemble learning framework by utilizing sophisticated machine learning models such as CatBoost classifiers, Naive Bayes Gaussians, decision trees, and support vector machines (SVMs). This framework uses fused data to estimate which crops will be most suited.CRS also has an easy-to-use interface that can be accessed on mobile and web platforms. With this interface, farmers may input the properties of their soil and get recommendations for treatments in real-time.To ensure the system’s reliability and scalability, we tested the system extensively across a variety of geographic locations and climate zones. The efficacy of CRS in recommending crops for various soil and climate conditions is verified by performance metrics like accuracy, precision, and recall. A promising accuracy rate of more than 95% is demonstrated by the data, demonstrating how useful CRS is in assisting farmers in selecting the best crops. In order to increase agricultural productivity and sustainability, farmers can use the intelligent decision support system developed by this research to acquire data-driven insights.