Automated Seed Quality Testing System using GAN and Active Learning

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) annotating 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 and achieve accuracies of up to 91.6% for testing the physical purity of seed samples.

In 9th International Conference on Pattern Recognition and Machine Intelligence (PReMI 2021)