Automated Seed Quality Testing System using GAN & Active Learning

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.

Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning

“…The dataset used in this study is the publicly available Corn Seeds Dataset [42] provided by a laboratory in Hyderabad, India. This dataset encompasses a collection of 17,801 images of corn seeds, which are classified into four distinct categories: pure, broken, discolored, and silkcut.…”

“…Further breakdown of the diseased seeds reveals that 32% of them are broken, 17.4% are discolored, and 9.8% are silkcut. The Corn Seeds Dataset [42] serves as a valuable resource for researchers and practitioners in the field of corn disease identification. It provides a diverse set of seed images, encompassing both healthy and diseased samples, enabling the development and evaluation of deep learning models specifically tailored for corn disease classification.…”