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CS-1917: AgriVision: AI-Driven Crop Disease Detection, Agri- Marketplace Integration, and Drone-Based Spray Services
Author(s):
1. Syed Hassaan Ali Shah: Faculty of Computing, Riphah International University,Islamabad,Pakistan
2. Muhammad Uzair: Faculty of Computing, Riphah International University,Islamabad,Pakistan
3. Abu Bakar: Faculty of Computing, Riphah International University,Islamabad,Pakistan
4. Saad Jamil: Faculty of Computing, Riphah International University,Islamabad,Pakistan
Abstract:
Agricultural productivity is significantly threatened by crop diseases,particularly in staple crops such as wheat and rice. This project introduces anAI-powered crop management system that integrates disease detection, decisionsupport, and service delivery within a single platform. Using advanced imagerecognition, the system identifies common diseases in wheat and rice andprovides tailored medicine recommendations. An integrated e-commercemodule enables farmers to purchase the suggested medicines directly frommultiple vendors, ensuring availability and competitive pricing. Beyond productaccessibility, the platform connects farmers with agricultural experts throughvideo consultations, offering personalized guidance for effective diseasemanagement. To further support field- level implementation, drone-basedspraying services are incorporated, ensuring precise and efficient application oftreatments. By unifying intelligent disease detection, digital commerce, expertconsultation, and drone-enabled services, this system delivers a holistic solutionthat empowers farmers, minimizes crop losses, and promotes sustainableagricultural practices.
Page(s): 98-98
DOI: DOI not available
Published: Journal: 4th International Conference of Sciences “Revamped Scientific Outlook of 21st Century, 2025” , November 12,2025, Volume: 1, Issue: 1, Year: 2025
Keywords:
machine learning , Precision Agriculture , Artificial intelligence , Computer vision , smart farming , Crop Disease Detection , DroneBased Spray System
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