SeedGrade
AI-powered seed quality assessment system utilizing computer vision for instant grading validation.
01 // Overview & Problem Statement
SeedGrade is an interdisciplinary solution bridging agricultural operations with edge-based AI. It aims to replace tedious manual inspections of agricultural seeds with automated visual analysis.
Traditional seed classification and quality grading rely on physical sampling under magnifying glasses or slow laboratory tests. This process introduces human subjectivity and slows down supply chains.
02 // Methodology & Workflow Pipeline
Using a mobile setup equipped with macro-lenses, we capture high-resolution seed spreads under consistent lighting. The visual input is evaluated by image processing and machine learning classifiers to determine physical compliance, damages, and foreign matter.
03 // The Solution & My Contribution
Designed the OpenCV image preprocessing pipeline and custom web interface. Built algorithms to crop individual seed segments and classify them according to standard agricultural grading grids.
Created the frontend testing dashboard, wrote image segmenters, and structured validation scripts.
Technical Roadblock & Resolution
Varying seed colors and glares from light sources created contour detection gaps. Solved by implementing adaptive thresholding and normalized color-masking pipelines.
LESSONS LEARNED
Agricultural domain parameters must be translated into pixel tolerances; field validation is essential to align mathematical algorithms with agricultural realities.
FUTURE ROADMAP
- Incorporate deep learning YOLO networks for highly occluded seed detection.
- Implement bluetooth scale sync for combined weight-volume density classification.
Project Dossier
Evaluation Results
84.2% average classification agreement with standard manual lab grading tests.
100% rejection rate for invalid non-seed inputs, preventing system classification noise.
187 ms average edge processing latency per sample analysis.