Automating Genetic Purity Testing in Seed QC Using Multi-Stage Deep Learning

The Problem: A Bottleneck in Seed Quality Control

In seed production facilities, every batch must pass a critical test called Other Differntial Varieties (ODV) or Genetic Purity Testing before reaching farmers. Quality control experts manually inspect thousands of seeds per batch, comparing physical characteristics—size, shape, color, texture—against the target variety (in this case, Shree101). Only batches with high genetic purity (95%+) are cleared for sale.

The manual process is brutal:

• Time-intensive: Inspecting 10,000+ seeds per batch takes hours
• Requires expertise: Distinguishing visually similar varieties demands trained personnel
• Human fatigue: Consistency drops after hours of inspection
• Throughput bottleneck: Limits the number of batches that can be processed daily

Business Impact: Delayed shipments, expensive skilled labor, and the constant risk of human error releasing contaminated batches to market.

Why This Problem is Insanely Hard

Challenge #1: Visual Similarity Hell

Imagine trying to distinguish between two grains of rice that look 99% identical—one is slightly rounder, the other has a subtly different tip shape. Now do that for 10,000 seeds. Welcome to my problem.

The target variety Shree101 is a "super-fine" seed that looks nearly identical to other super-fine varieties like JSR, Chintu, YSR, RNR, and Elito. Even trained experts squint at these under magnifying glasses. The visual differences are:

• Subtle shape variations (length-to-width ratio differences of <5%)
• Minor color gradients (requiring consistent lighting)
• Tiny texture differences at the seed tip

This is a fine-grained classification problem on steroids.

Challenge #2: The Data Nightmare