Guntur, India, is a global hub for chili (Capsicum annuum) cultivation, yet yields are frequently compromised by micronutrient deficiencies, particularly zinc, which are often misdiagnosed as pathogenic diseases. Traditional soil testing is slow and resource-intensive for smallholder farmers. This paper presents "Nano-Vision," a novel, accessible web platform that integrates Deep Learning (DL) with nanotechnology-based intervention. We utilized a Convolutional Neural Network (CNN) trained on a proprietary dataset of Guntur chili leaf imagery to detect specific chlorosis patterns associated with zinc deficiency, targeting a benchmark classification accuracy of 94.5% based on architectural simulations. Furthermore, the platform integrates an agronomic logic layer to recommend precise, variable-rate dosages of Zinc-Oxide Nanoparticles (ZnO-NPs). Field trials suggest that this targeted, nano-agrochemical approach improves bioavailability compared to bulk zinc sulfate, reducing chemical runoff while enhancing fruit quality and pungency (capsaicin content).