Case Study
See how T-Pest has helped agricultural agencies achieve earlier detection, lower pesticide costs, and smarter crop protection.
T-Pest - Smart Agricultural Farming
Project Highlights: An AI-powered solution developed for the Department of Agriculture to enable early detection, identification, and warning of crop pests and diseases through real-time field monitoring.
About Client: Developed for the Department of Agriculture, the solution focuses on supporting farmers in planning appropriate pesticide spraying schedules and optimizing crop productivity.
Client Challenges:
- Slow detection of pests and crop diseases in the field due to the lack of an early identification and warning system.
- Difficulties in monitoring crops in real time, limiting the ability to make timely intervention decisions.
- Lack of tools to support farmers in planning accurate pesticide spraying schedules, leading to higher risks of disease outbreaks and increased operational costs.
Solutions:
- Computer Vision & Object Detection: Applying computer vision technology to detect pests and diseases with an accuracy rate of up to 88%.
- IoT System (Sensor Devices + Edge AI): Enabling real-time processing and detection directly on edge devices deployed in the field.
- MLOps Integration: Integrating the MLOps system with cloud-based or on-premise servers for efficient model deployment and management.
- Object Counting / Tracking: Providing object counting and tracking capabilities to monitor and manage pest population density effectively.
Benefits:
- Flexible Deployment: Supporting both Cloud and On-premise platforms based on customer infrastructure requirements.
- Proactive Prevention: Helping farmers proactively prevent and manage pest and disease outbreaks.
- Cost Optimization: Improving production efficiency while minimizing pesticide usage and operational costs.
- Risk Reduction: Reducing the risk of disease outbreaks and optimizing overall agricultural productivity.