HarvestMax: A Predictive Model for Crop Yield and Fertilizer Optimization

In the backdrop of India's agrarian-centric economy, the precision of crop yield prediction and the adoption of optimal farming practices emerge as critical components for sustainable agricultural development. This research paper introduces an innovative methodology that integrates data mining techniques, machine learning algorithms such as Support Vector Machines (SVM), and Regression algorithms, along with a comprehensive attribute analysis to establish a resilient crop yield prediction and recommendation system. The proposed system draws insights from a diverse range of attributes crucial for agriculture, including geographical location, soil pH for alkalinity assessment, nutrient percentages (Nitrogen, Phosphorous, and Potassium), real-time weather conditions sourced from third-party APIs, soil type, nutrient composition, and regional rainfall data. By amalgamating and scrutinizing these multifaceted attributes, our system aspires to furnish farmers with precise and dependable predictions regarding their crop yields.