Advanced International Journal of Multidisciplinary Research

E-ISSN: 2584-0487 •   Impact Factor: 9.11

An Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Crop Disease Identification & Management System

Author(s) Mr. S. Jayanna, Bussa Tejaswini, Mohanty Smruthi Rekha, Maddela Akhila
Country India
Abstract Agricultural sustainability is frequently compromised by the delayed identification of crop pathologies and a lack of integrated recovery protocols. While traditional diagnostic technologies primarily focus on standalone image recognition, they often neglect the contextual influence of a farm’s longitudinal data. This research presents an intelligent crop disease identification and management framework that utilizes a dual-input approach, combining deep learning-based image analysis with historical crop records to enhance diagnostic precision. By employing Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN), the system classifies foliar diseases and correlates them with past environmental conditions to offer tailored agricultural advice. Beyond mere detection, the system generates automated reports featuring actionable treatment suggestions and long-term preventive strategies designed to mitigate future outbreaks. Experimental results demonstrate that this holistic methodology not only improves real-world detection accuracy but also gives farmers the ability to make well-informed decisions that maximize yield and promote sustainable farming practices.
Keywords Deep Learning, Convolutional Neural Networks (CNN) , Artificial Neural Networks (ANN), Crop Disease Management, Automated Diagnosis, Historical Data Analytics, Sustainable Agriculture, Image Processing
Discipline Engineering
Published In Volume 4, Issue 2, March-April 2026
Published On 2026-04-06
DOI https://doi.org/10.62127/aijmr.2026.v04i02.1274
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