Automated Seed Quality Testing System using GAN & 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...

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Bibliographic Details
Published inarXiv.org
Main Authors Nagar, Sandeep, Pani, Prateek, Nair, Raj, Varma, Girish
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.10.2021
Subjects
Active learning
Agricultural wastes
Annotations
Automation
Computer vision
Datasets
Generative adversarial networks
Image acquisition
Image classification
Quality assessment
Seeds
Vision systems
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Summary: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.
ISSN:2331-8422