Automated Seed Quality Testing System using GAN & Active Learning
- URL: http://arxiv.org/abs/2110.00777v1
- Date: Sat, 2 Oct 2021 10:28:25 GMT
- Title: Automated Seed Quality Testing System using GAN & Active Learning
- Authors: Sandeep Nagar, Prateek Pani, Raj Nair, Girish Varma
- Abstract summary: We build a novel seed image acquisition setup, which captures both the top and bottom views.
We are able to get accuracies of up to 91.6% for testing the physical purity of seed samples.
- Score: 2.8978926857710263
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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