Authors: Mr. V. Gopinath, V. Aasritha Devi, P. Deekshitha, V. Pragna, P. Siva Sankara Rao
Abstract: Global food security is currently challenged by a dual-front crisis: a non-linear surge in the global population and the concurrent, unpredictable degradation of arable land, as highlighted by the United Nations [18]. Traditional agricultural methodologies frequently depend on generalized fertilizer applications that fail to account for site-specific soil chemistry, leading to nutrient runoff or stunted growth (Wolfert et al. [19]). Building upon the foundational web-based and mobile frameworks established by Agri Vision Pro [1] and AgroVision et al. [2], this research introduces AgroVision Pro. AgroVision Pro is a high-fidelity, multi-stage machine learning framework designed to eliminate guesswork by integrating classification and regression pipelines into a cohesive decision-support ecosystem. Utilizing state-of-the-art algorithms, including XGBoost (Chen et al. [9]) and Random Forest (Breiman [10]), the platform achieves a 93.2% accuracy in crop selection and an R^2 score of 0.89 in yield quantification. This research demonstrates how localized soil data, processed through an innovative "Feature-Chaining" architecture, transitions agriculture from a reactive industry to a proactive, precision-driven powerhouse.
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