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AI-Powered Plant Disease & Pest Detection (PoC)
Overview
At TRCOMS Ltd, we are constantly exploring how AI can drive innovation in critical sectors. As part of our R&D programme, we developed a trained machine learning model that demonstrates the potential for AI-powered pest and plant disease detection.
This proof of concept (PoC) lays the groundwork for future development of a scalable system that can help farmers detect and monitor crop health issues early, reduce pesticide usage, and improve yield through data-driven agricultural practices.
Challenges
Pests and plant diseases cause billions in crop losses every year. Traditional methods rely on:
- Manual inspection (slow, resource-intensive).
- Blanket pesticide use (costly, environmentally harmful).
- Lack of real-time data for timely interventions.
The challenge was to create a trained AI model capable of distinguishing between healthy plants and diseased crops — forming the basis for smarter agricultural tools in the future.
Solution
We developed and trained an AI model designed to:
- Detect Pest & Disease Symptoms
- Differentiate between healthy plants and those affected by pests/diseases.
- Enable Real-Time Monitoring (Future Potential)
- While still at PoC stage, the model is designed to integrate with real-time monitoring tools.
- Support Integrated Pest Management (IPM)
- Potential to combine with weather data and pest lifecycle data for sustainable interventions.
- Reduce Reliance on Chemicals
- By enabling targeted detection, farmers could one day use less pesticide and adopt eco-friendly practices.
Implementation Highlights
- AI Model Training – Built a computer vision model capable of identifying plant health conditions.
- Proof of Concept – The trained model validates feasibility for future large-scale development.
- Trivoh Integration Potential – Designed to be extensible with communication platforms like Trivoh, enabling farmers to access alerts and expert consultations remotely.
- Internship Innovation – Developed under our AI internship programme, demonstrating our commitment to building future talent while exploring sector-wide impact.
Result and Future Outlook
- The trained model successfully identified pest/disease presence in test datasets.
- Demonstrated that AI can play a key role in early crop protection.
- Provides a foundation for future product development, including:
- Real-time farmer dashboards.
- Integration with IoT devices like smart traps and sensors.
- Mobile app interfaces for remote diagnostics.
This project is an early-stage prototype, but it highlights TRCOMS’ ability to bridge AI research with practical, real-world applications.
Why It Matters
Food security depends on smarter farming practices. With AI-driven detection models like this, there is huge potential for:
- Sustainable pest management.
- Reduced chemical usage.
- Increased agricultural productivity.
TRCOMS is committed to developing this further in collaboration with agritech innovators, government agencies, and research partners
Tech stack
Python & React Java Script
Published:
November 16th, 2024
Category:
AI & MACHINE LEARNING
Product:
AI-Powered Plant Disease & Pest Detection (POC)
COMPANY:
Triumphant Communications
