Published Online:April 2026
Product Name:The IUP Journal of Computer Sciences
Product Type:Article
Product Code:IJCS040326
DOI:10.71329/IUPJCS/2026.20.2.28-43
Author Name:Rishitosh Mondal, Prarambhika Bhattacharjee, Piyush Dawn, Koushiki Ghosh, Debolina Saha and Sudip Dogra
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:28-43
Crop pests and plant diseases significantly threaten agricultural productivity, particularly in rural regions with limited access to expert support and reliable internet connectivity. This paper presents CropGuard—a farmer-centric mobile decision support system—for realtime pest and disease detection. The proposed framework utilizes a YOLOv8-based object detection model deployed on-device to enable low-latency and offline image analysis. Detected diseases are linked to an expert-curated recommendation engine derived from official government agricultural portals, providing validated preventive and treatment guidance through a simplified multilingual interface supporting five languages. To enhance field-level usability, the system incorporates offline result storage and notification delivery. Experimental evaluation demonstrates reliable detection performance suitable for realworld agricultural deployment, effectively bridging the gap between advanced computer vision techniques and accessible, practical farming solutions.
Crop pests and plant diseases continue to pose serious challenges to agricultural productivity, particularly in regions where access to timely expert guidance is limited.