Version 1
: Received: 13 August 2024 / Approved: 13 August 2024 / Online: 13 August 2024 (13:06:34 CEST)
How to cite:
Gebremeskel, G. B.; Gashaye, D. Groundnut (ARACHIS HYPOGAEA L.) Seed Defect Classification Using Ensemble Deep Learning Techniques. Preprints2024, 2024080948. https://doi.org/10.20944/preprints202408.0948.v1
Gebremeskel, G. B.; Gashaye, D. Groundnut (ARACHIS HYPOGAEA L.) Seed Defect Classification Using Ensemble Deep Learning Techniques. Preprints 2024, 2024080948. https://doi.org/10.20944/preprints202408.0948.v1
Gebremeskel, G. B.; Gashaye, D. Groundnut (ARACHIS HYPOGAEA L.) Seed Defect Classification Using Ensemble Deep Learning Techniques. Preprints2024, 2024080948. https://doi.org/10.20944/preprints202408.0948.v1
APA Style
Gebremeskel, G. B., & Gashaye, D. (2024). Groundnut (ARACHIS HYPOGAEA L.) Seed Defect Classification Using Ensemble Deep Learning Techniques. Preprints. https://doi.org/10.20944/preprints202408.0948.v1
Chicago/Turabian Style
Gebremeskel, G. B. and Dinkie Gashaye. 2024 "Groundnut (ARACHIS HYPOGAEA L.) Seed Defect Classification Using Ensemble Deep Learning Techniques" Preprints. https://doi.org/10.20944/preprints202408.0948.v1
Abstract
Groundnut is an oil seed cash crop, that can be consumed directly by humans and animals. It is also used as an intelligence in the industrial production of butter and other products. However, Groundnut seeds can become infected with fungi, viruses, pests, physical damage, and high heat, which can negatively impact crop yield, quality, and economic value, and pose health hazards. To address these challenges, computer vision technology detects and classifies defects. This study introduced an ensemble deep learning defected classification model that combines VGG16 and InceptionV3 using seed images. To enhance model performance, collected images are preprocessed using novel techniques tailored to different image types. Preprocessing techniques are chosen based on image-quality evaluation metrics. Watershed segmentation is applied followed by detecting the region of interest in the image using YOLOv3. The image dataset is augmented and balanced using a Generative Adversarial Network (GAN). The model development involves a combination of classical and deep-based features, comparing features extracted with (HOG and GLCM) to those extracted with InceptionV3 and VGG16. The ensemble model achieves an accuracy of 96.25% with a split ratio of 10% for testing, 10% for validation, and 80% for training set. This research benefits farmers looking to improve yield, consumers of groundnut products, and researchers studying seed defects. The work provides valid insights for future researchers regarding dataset creation, augmentation, methods, and feature selection for modeling. However, the model experienced misclassifications due to the similar appearance of sample images in different classes. Future researchers should address these challenges and consider factors such as oil content and defect grading.
Keywords
deep ensemble learning feature extraction; Generative Adversarial Network; Histogram of Oriented Gradients; Visual Geometry Group
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
