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Development of a Full-Scale Plant Pathology Recognition Platform with Enhanced ML Capabilities and IoT Integration

itransition.com

Agriculture

Environmental Services

Medical

Challenges in Plant Pathology Identification and Data Analysis

Existing plant pathology solutions lack advanced analysis capabilities for grouping samples, filtering irrelevant data, and providing real-time IoT-enabled diagnostics. Manual processes for sample comparison and analysis create inefficiencies for field scientists and laboratories, while limited integration with modern scanning devices hampers research mobility and accuracy.

About the Client

A software startup specializing in AI-driven agricultural diagnostics and pathology identification solutions

Key Goals for Platform Development

Create a scalable plant pathology recognition platform based on the successful PoC
Enhance ML model accuracy beyond 80% KPI achieved in the PoC phase
Establish integration with IoT scanning devices for field research
Develop collaborative tools for scientific institutions
Enable commercialization through investor-ready demonstration capabilities

Core System Functionalities and Features

Mobile application for IoT device integration and image capture
Automated image preprocessing and quality optimization
ML-based pathology classification using ResNet50 models
Multi-format image support (RAW, ProRAW)
Cloud storage integration with Azure Blob Storage
Web portal for expert validation and collaborative analysis
Multi-tenant architecture for scientific institutions
Data synchronization and batch processing capabilities

Technology Stack Requirements

Python (ML pipelines)

.NET (backend)

Swift (iOS application)

React (web application)

Azure (cloud infrastructure)

PyTorch (neural network training)

Tailwind CSS (frontend styling)

System Integration Requirements

IoT scanning device APIs
Azure Blob Storage
Third-party ML libraries
Mobile device camera hardware
User authentication services

Non-Functional Requirements

High scalability for handling large image datasets
Low-latency image processing
Data security and privacy compliance
Cross-platform compatibility
High availability for scientific users
Robust error handling for field conditions

Expected Business and Industry Impact

The platform will revolutionize plant pathology research through automated analysis and IoT-enabled field diagnostics, enabling faster investment acquisition, expanding scientific collaboration networks, improving diagnostic accuracy for agricultural research institutions, and establishing a commercial foundation for future AI-driven agricultural solutions.