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Phyto Pathogen Perception: AI Framework for Early Plant Disease Diagnosis and Sustainable Crop Management

1st M. Vasuki, 2nd C Nandha Kumar, 3rd E Sriram

1Associate Professor, Department of computer Applications, Sri Manakula Vinayagar Engineering College (Autonomous), Puducherry 605008, India

2Post Graduate student, Department of computer Applications, Sri Manakula Vinayagar Engineering College (Autonomous), Puducherry 605008, India

3Post Graduate student, Department of computer Applications, Sri Manakula Vinayagar Engineering College (Autonomous), Puducherry 605008, India

*Corresponding author’s email address: sriramelumalai2002@gmail.com

Abstract: Phyto Pathogen Perception is an advanced Python-based web application designed to revolutionize agriculture by offering early and precise plant disease diagnosis. Developed with the Flask framework, the platform provides a user-friendly interface for farmers, researchers, and agricultural experts to upload plant images and receive instant, detailed diagnostic reports. Leveraging cutting-edge machine learning through PyTorch and TorchVision, the platform ensures high-accuracy disease classification using deep learning models trained on diverse and continuously updated datasets, including rare and emerging diseases, for greater reliability across various plant types and environments.To enhance accessibility and scalability, the platform features cloud-based storage and processing, enabling fast analyses and real-time updates even during peak usage. Multilingual support allows users from different regions to access diagnostic reports and prevention tips in their native languages. Real-time alerts and predictive analytics, driven by environmental factors like temperature and humidity, empower users to manage diseases proactively. Integration with IoT devices such as soil sensors and weather monitors provides comprehensive insights into crop health and environmental conditions influencing disease prevalence.The platform’s robust data handling capabilities, powered by Pandas and NumPy, enable efficient analysis and actionable insights, while image preprocessing tasks such as resizing, cropping, and augmentation are seamlessly managed using the Pillow library to optimize input for machine learning models. Tailored disease management plans, including customized recommendations for pesticide use, organic solutions, and crop rotation strategies, enhance user outcomes.To foster collaboration, a community forum enables farmers and experts to share knowledge and feedback. An AI-powered chatbot provides instant guidance on disease prevention and management, improving user engagement. The integration of e-commerce platforms simplifies access to recommended agricultural products, while crop yield optimization features support better productivity. Gamification elements educate users about plant diseases, prevention techniques, and best farming practices, making the platform both interactive and impactful.

Keywords: Crop health, Sustainable agriculture, Disease classification, Image preprocessing, K learning Algorithms, Flask framework.