Machine Learning Towards Sustainable Agriculture for Crop Recommendation

Despite being one of the most important industries for human survival, farmers frequently struggle to choose the best crop to grow given the soil and environmental factors. This study introduces a machine learning-driven crop recommendation system that gathers real-time data on soil pH, moisture, temperature, and humidity using IoT sensors. After being uploaded to a web-based platform, the gathered data is processed by **machine learning algorithms to recommend the best crops based on predictive modeling and historical data. Utilizing real-time analytics and AI-based decision making, the system optimizes resource utilization and increases crop yield by offering individualized recommendations. Farmers can upload sensor data, view analytical insights, and access recommendations in an interactive and user- friendly way through the web application, which acts as their main interface. The platform ensures scalability and accessibility by integrating cloud computing for the storage and analysis of large datasets. Additionally, the system offers adaptive learning mechanisms and *automated alerts, allowing farmers to modify their plans in response to shifting environmental conditions. The findings show that the suggested system **improves the accuracy of crop selection, lessens reliance on conventional farming practices, and encourages data-driven precision agriculture.