Cotton cultivation is an important source of agricultural income in the Vidarbha region of Maharashtra, yet disease outbreaks can threaten yield and increase crop-protection costs. Deep-learning computer-vision models, including MobileNetV2 and ResNet-50, offer promising ways to identify foliar symptoms from leaf images. However, technical performance alone does not ensure adoption by smallholder farmers. This paper examines the commercial viability and go-to-market options for AI-powered cotton disease diagnostic tools from an agri-business perspective. It uses the Technology Acceptance Model (TAM), supplemented by the Unified Theory of Acceptance and Use of Technology (UTAUT), to frame adoption around perceived usefulness, ease of use, price sensitivity, digital access, and trust in local agricultural advisers. A per-acre cost-benefit illustration compares conventional scouting and spraying with a proposed AI-supported targeted-intervention model. The supplied illustrative table estimates total diseasemanagement and production-loss outlays of INR 15,500 under conventional management and INR 6,200 under the proposed model, implying a difference of INR 9,300 per acre. These figures are scenario estimates and require validation against field records and local agronomic costs before being treated as measured savings. The paper also compares direct-to-consumer subscriptions with business-to-businessto-consumer partnerships involving Krishi Seva Kendras, farmer producer organizations, and agricultural input companies. It proposes low-bandwidth vernacular interfaces, seasonal pricing, field demonstrations, and institutional validation as ways to reduce adoption barriers. The analysis concludes that scalable commercialization depends on combining reliable diagnostics with farmer trust, affordable access, and viable rural distribution partnerships.

Swapnil Vilas Juware, Dr. Atul B. Tekade, "Commercial Adoption of AI-Based Cotton Disease Diagnostics in Vidarbha" International Journal of Scientific Research and Engineering Development, V9(5): Page(1958-1961) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.