The Plant Disease Diagnostic (PlantDiag) platform is an innovative solution designed to revolutionize plant disease diagnosis and pathogen detection. This comprehensive project facilitates international collaboration among research institutions, leveraging a robust HW/SW Cloud infrastructure and online web-mobile services to address global food security challenges.

At the heart of PlantDiag is the eLegere Platform, which powers the design of web database structures and smart application modules. These modules streamline a workflow that connects service providers—like research centers, universities, and laboratories—with end-users, including ICT villages and government institutions, especially in underdeveloped countries.

PlantDiag introduces a suite of innovative services and technologies that support the development of rapid, efficient food security and safety policies. By streamlining the workflow from disease detection to diagnosis and recommending best-practice care, PlantDiag integrates both traditional and innovative knowledge resources.

Key features of the PlantDiag platform include:

• Advanced Technologies: Incorporating Internet of Things (IoT), wearable technologies, and mobile devices to collect and process data, using AI, Machine Learning, and Predictive Analytics to provide actionable insights.
• Detailed Workflow: A step-by-step process from disease detection to diagnosis and management, enabling end-users to submit diagnosis requests through a web/mobile app, which are then validated and processed by district and national centers before reaching service providers.
• Interoperability: Ensuring seamless integration with external data sources and intelligent services, offering a flexible and comprehensive solution to various stakeholders.
• User-Centric Design: Accessible on desktop, tablet, and mobile with smart data input options like image, text, voice recognition, and geolocation, and secure user profiles that track data history.
• Data Management: A centralized data collection, validation, and sharing system with user permissions, combined with georeferenced data navigation for enhanced decision-making.

The PlantDiag system's primary workflow is straightforward yet comprehensive. End-users can capture plant photos and submit detailed information for diagnosis. District services validate these requests, providing initial feedback and forwarding them to national centers, which in turn analyze the data, monitor environmental conditions, and collaborate with service providers for a final diagnosis. This process not only resolves individual cases but also enriches a knowledge base for AI and Machine Learning services.

The anticipated impact of PlantDiag is extensive, offering tools to conserve and enhance natural resources, improve soil conditions, and sustainably increase farm income. It aims to scale and transfer these solutions to other regions, thus enhancing food security and safety policies on a global scale.

Future steps for the PlantDiag Project include the integration of databases and web services for intelligent plant disease recognition based on field photographs, utilizing technologies like Microsoft Azure Cognitive Services. Further developments will also focus on creating devices for detecting and diagnosing plant issues, managing external devices and IoT sensors, and integrating results into the support request verification process.

By combining a user-friendly interface with cutting-edge technology, PlantDiag stands as a testament to digital transformation in agriculture, propelling the industry towards a sustainable and secure future for food production worldwide.