International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Silk Vision: An AI-Powered Web Platform for Automated Silkworm Disease Detection Using Deep Learning

Author(s) Chaitra H. V., Jeevan K. P., Dr. P. Sandhya
Country India
Abstract Silkworm cultivation serves as a fundamental economic pillar in many rural regions, providing agricultural households with vital opportunities for income diversification. However, the high susceptibility of silkworms to various infectious diseases presents a constant threat of catastrophic population declines and significant economic distress. Early identification of these issues is currently hindered by a reliance on traditional visual inspections conducted by trained professionals—an approach that is often cost-prohibitive and inaccessible for small-scale farming operations. Such diagnostic delays lead to increased mortality rates and decreased production standards.This paper proposes an automated, AI-driven web-based platform designed to provide accessible pathological assessment through computational intelligence and pattern recognition. The system features a secure user interface where farmers can upload digital images of silkworm specimens to receive immediate analytical feedback. By employing a technical methodology cantered on advanced image processing, data augmentation, and Convolutional Neural Network (CNN) based classification, the platform automatically detects visual indicators of common ailments such as Pebrine, Grasserie, Flacherie, and Muscardine. Results demonstrate that these techniques enhance classification accuracy and system performance across diverse environments, effectively reducing dependence on manual expertise and supporting sustainable sericulture through smart agriculture.
Published In Conference / Special Issue (Volume 8 | Issue 3) - Two-Day National Conference on “Women Led Development: Pathways to Inclusive, Sustainable, & Equitable Growth” (DePaul-2026) (May 2026)
Published On 2026-05-03
DOI https://doi.org/10.36948/ijfmr.2026.DePaul-2026.1904
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E-ISSN 2582-2160
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