Development of an AI-Powered Plant Pathology Recognition System

The client faces difficulties in accurately and efficiently identifying plant pathologies from images, especially due to limited reference data and the need for a system that can compare and analyze plant images on the go. Current solutions lack comprehensive analysis capabilities, integration with IoT devices, and efficient data processing, hindering research progress and timely diagnosis.

A mid-sized research-focused organization specializing in plant health diagnostics and agriculture sciences, seeking to leverage machine learning for rapid plant disease identification and analysis.

• Develop a machine learning-based system capable of accurately identifying plant sample types and associated pathologies with a target accuracy of at least 80%.
• Create a mobile application integrated with plant scanning devices for real-time image capture and upload to a centralized cloud platform.
• Implement a web-based interface for scientific experts to review, analyze, and validate machine predictions, supporting multi-user and multi-organization access.
• Ensure seamless interoperability between mobile devices, image scanners, and cloud storage solutions.
• Generate synthetic training data to supplement limited reference images, enhancing model robustness.

• Mobile application for capturing plant images compatible with RAW and ProRAW formats, connected to a scanner device.
• Automatic upload of images to a cloud storage service for processing.
• Backend processing pipeline that preprocesses images, performs classification of sample types, and predicts pathology categories using trained neural networks.
• Synthetic data generation processes to augment training datasets and improve model accuracy.
• Web portal for scientists and experts to review analysis results, provide final validation, and manage user access in a multi-organization environment.
• Interoperability modules to connect with various scanning hardware and IoT devices.

• Cloud storage service (e.g., Azure Blob Storage) for image data management.
• Scanning devices or sensors for image capture and real-time data transfer.
• IoT platforms if needed for on-the-go plant analysis connectivity.
• User authentication and management systems for secure multi-organization access.

• System should process and analyze image data within a maximum of 2 minutes per image set for real-time usability.
• Achieve at least 80% accuracy in pathology prediction models.
• Ensure data security and compliance with relevant standards for sensitive research data.
• Design for scalability to support increasing data volume and concurrent users, aiming for at least 1000 users within the first year.

The implementation of this AI-powered plant pathology recognition system is expected to significantly improve diagnostic accuracy to over 80%, accelerate plant health research workflows, facilitate collaboration among scientific institutions, and attract further investment and partnership opportunities by demonstrating cutting-edge capabilities in plant disease analysis.