Automated Seed Quality Testing System using GAN & Active Learning

• Main Authors: Nagar, Sandeep, Pani, Prateek, Nair, Raj, Varma, Girish
• Format: Journal Article
• Language: English
• Published: 02.10.2021
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Hardware Architecture
• DOI: 10.48550/arxiv.2110.00777

9th International Conference on Pattern Recognition and Machine Intelligence 2021 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.