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Detailed Description
Developed an AI-powered computer vision system for early pest detection in watermelon crops. Using trained models (Python, TensorFlow) and real-time camera feeds, the system identifies anomalies to boost rural farming efficiency. Built a Node.js backend, cloud processing pipeline, and monitoring dashboard. This project supports digital transformation in Colombian agriculture through a local cooperative initiative.
The Challenge
Create an automated pest detection system that improves agricultural efficiency and supports digital transformation in Colombian rural cooperatives.
Our Solution
We developed an AI-powered computer vision system using TensorFlow-trained models, real-time processing, and a monitoring dashboard for early pest detection.
Results Achieved
System in development that promises to significantly improve agricultural efficiency and contribute to the digital transformation of the Colombian agricultural sector.
Project Timeline
Analysis
2 weeksInitial research and planning
Design
3 weeksWireframes and prototypes creation
Development
8 weeksFeature implementation
Testing
2 weeksTesting and optimization
Launch
1 weekDeployment and monitoring
Project Details
Coopel LTDA
1 year
In Development
ai-ml
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