Automated Seed Quality Testing System using GAN & Active Learning
This addresses the need for efficient, automated quality control in agriculture to reduce food waste, though it is an incremental improvement specific to seed testing.
The paper tackles the problem of automating seed quality assessment, which is currently manual and expert-dependent, by proposing a computer vision system that achieves up to 91.6% accuracy in testing seed physical purity.
Quality assessment of agricultural produce is a crucial step in minimizing food stock wastage. However, this is currently done manually and often requires expert supervision, especially in smaller seeds like corn. We propose a novel computer vision-based system for automating this process. We build a novel seed image acquisition setup, which captures both the top and bottom views. Dataset collection for this problem has challenges of data annotation costs/time and class imbalance. We address these challenges by i.) using a Conditional Generative Adversarial Network (CGAN) to generate real-looking images for the classes with lesser images and ii.) annotate a large dataset with minimal expert human intervention by using a Batch Active Learning (BAL) based annotation tool. We benchmark different image classification models on the dataset obtained. We are able to get accuracies of up to 91.6% for testing the physical purity of seed samples.