Agriculture
Automated Grain-Quality Classification for Agriculture Commodity Business
Client
Global Health & Wellness Leader
Industry
Agriculture
Services
Computer Vision
Duration
Enterprise Grade
Executive Summary
Engineering resilience through Global Health & Wellness Leader's transformation.
We leveraged deep learning to automate the intricate process of grain quality inspection, significantly reducing human error and increasing operational throughput for a global commodity business.
The Quality Challenge
- Modernize the manual inspection process, which was historically slow and prone to subjective error.
- Eliminate grading inconsistencies that led to pricing disputes and supply chain friction.
- Design a robust mobile solution that remains fully functional in remote, low-connectivity agricultural regions.
The Vision-First Solution
- Developed a custom Convolutional Neural Network (CNN) specifically for high-accuracy multi-class grain classification.
- Optimized deep learning models for efficient edge deployment on standard mobile devices.
- Implemented an offline-first data synchronization architecture for reliable field operations.
Measurable Outcomes
Operational Success
95%
Classification accuracy achieved, consistently outperforming expert human graders in the field.
10x
Increase in inspection speed at commodity collection centers through automated validation.
Standardizing Quality Across the Agriculture Supply Chain
Our computer vision solution provided a reliable and objective benchmark for commodity pricing. This automation creates a transparent ecosystem that benefits both local farmers and global commodity buyers.
Technical Stack
TensorFlowOpenCVAWS LambdaReact Native
Core Disciplines
Computer VisionAgricultureMobile Edge
