Soil Fertility Assessment and Crop Recommendation for Sustainable Farming using Machine Learning and Deep Learning

"Soil Fertility Assessment and Crop Recommendation for Sustainable Farming using Machine Learning and Deep Learning" represents a pioneering endeavor at the intersection of agriculture and data science. It harnesses a diverse dataset encompassing soil properties, historical rainfall data, and temperature records, meticulously collected from reliable sources. This project employs advanced machine learning algorithms to predict soil fertility levels accurately and offer precise crop recommendations based on the unique conditions of each agricultural region. By bridging the gap between traditional farming practices and cutting-edge technology, the initiative addresses key challenges faced by modern agriculture. It facilitates enhanced crop yields through optimized resource allocation and reduced environmental impact by tailoring planting schedules and nutrient management. Moreover, it fosters sustainability by promoting responsible land usage, which is crucial in the face of evolving climate patterns and the increasing global demand for food security. In essence, Precision Agriculture emerges as a transformative solution that empowers farmers with actionable insights, ushering in an era of data-driven farming practices. By combining the strengths of data science, environmental science, and agriculture, this project contributes significantly to the broader goals of sustainable farming, economic viability, and ecological resilience.The dataset consists of 2200 instances and 8 features. We train several machine models and also implement deep learning models to check for its performance. It was observed that the Naive Bayes shows 99.5 percent accuracy after hypertuning and with CNN showing the least at 87.1 percent accuracy. These conclusions help in building better recommendation systems and farming techniques.