Problem Statment
Plant leaf diseases pose a formidable threat to global food production, causing substantial crop losses and jeopardizing food security. Current plant leaf disease detection methods, primarily relying on visual inspection by farmers and experts, are time-consuming, labor-intensive, and prone to human error, resulting in delayed detection and ineffective disease management. These limitations often lead to the wasteful expenditure of time and resources, exacerbating plant losses. Current methods frequently fail to detect diseases in their early stages, when intervention is most effective, leading to widespread outbreaks and significant crop losses before diseases are identified and controlled. Accurately differentiating between various plant leaf diseases can be challenging, especially for diseases with similar symptoms, potentially leading to inappropriate treatment strategies and further crop losses. Existing methods may not be adaptable to diverse environmental conditions, crop varieties, and disease strains, limiting their effectiveness in a wide range of agricultural settings. Current plant leaf disease detection methods often lack integration with precision agriculture technologies, hindering data-driven decision-making and disease management strategies. Traditional methods can be costly and unsustainable, particularly for large-scale operations, potentially restricting access to effective disease detection and management practices. Therefore, an urgent need exists for a novel, automated and accurate plant leaf disease detection system to address these challenges and provide a reliable solution for early detection, effective disease management, and improved crop health and productivity.
Existing System/ Related Works
Papers:
DenseNet-77-based Corner Net model for the tomato plant leaf disease detection and classification
plant leaf disease detection and classification
Transfer learning based deep ensemble neural network for plant leaf disease detection
Cotton Leaf Net: cotton plant leaf disease detection
Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition
Apps:
Ai plant Care: https://apps.apple.com/pk/app/plant-doctor/id1483705696
plant lens plant identifier: https://apps.apple.com/pk/app/ai-plant-identification-app/id6471321457
Plant parent: https://apps.apple.com/pk/app/plantme-ai-plant-identifier/id1532875668
Plant doctor: https://apps.apple.com/pk/app/plant-parent-plant-care-guide/id1612792132
Agrio-plant health app: https://apps.apple.com/pk/app/agrio-plant-health-app/id1239193220
Plantix: https://apps.apple.com/pk/app/plantix-plant-leaf-identifier/id6450135619
Proposed System Modules
Admin Login
User Login
User Signup
Plant information & care
Search plant
Plants disease info
Top ten crops buttons
Real time plant leaf disease detection
History
Treatment Recommendation
Request for a specific plant
Logout
Advantage of the Proposed System
Early Disease Detection
Accurate Identification
User-Friendly Interface
Comprehensive Information
Integration with Other Modules
Educational and Research Benefits
Synopsis Document
Synopsis Presentation
App Screenshots
App Preview
Home Page
Team
Abdul Hanan Afzal (FC-025)
Ali Hassan(FC-015)
Saad Majeed (FC-057)