PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Revealing GLCM Metric Variations across Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications
Version 1
: Received: 23 April 2024 / Approved: 23 April 2024 / Online: 25 April 2024 (15:09:21 CEST)
How to cite:
Kabir, M.; Unal, F.; AKINCI, T.C.; Martinez-Morales, A.A.; Ekici, S. Revealing GLCM Metric Variations across Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications. Preprints2024, 2024041566. https://doi.org/10.20944/preprints202404.1566.v1
Kabir, M.; Unal, F.; AKINCI, T.C.; Martinez-Morales, A.A.; Ekici, S. Revealing GLCM Metric Variations across Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications. Preprints 2024, 2024041566. https://doi.org/10.20944/preprints202404.1566.v1
Kabir, M.; Unal, F.; AKINCI, T.C.; Martinez-Morales, A.A.; Ekici, S. Revealing GLCM Metric Variations across Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications. Preprints2024, 2024041566. https://doi.org/10.20944/preprints202404.1566.v1
APA Style
Kabir, M., Unal, F., AKINCI, T.C., Martinez-Morales, A.A., & Ekici, S. (2024). Revealing GLCM Metric Variations across Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications. Preprints. https://doi.org/10.20944/preprints202404.1566.v1
Chicago/Turabian Style
Kabir, M., Alfredo A. Martinez-Morales and Sami Ekici. 2024 "Revealing GLCM Metric Variations across Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications" Preprints. https://doi.org/10.20944/preprints202404.1566.v1
Abstract
The intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance underscores the need for rigorous dataset evaluation and selection protocols to ensure the reliability and generalizability of classification outcomes. This study involved a thorough examination of selected publicly available plant diseases datasets, with an emphasis on how well they performed as measured by GLCM metrics. After first classifying the datasets according to their GLCM metrics, dataset_2 (D2) and dataset_5 (D5) were found, respectively, to be the best-performing dataset in all GLCM analyses. The same datasets were then used to train deep learning models, and their classification performances were assessed. A noteworthy association was observed between the results of training deep learning models and the performance ratings derived from GLCM studies. More specifically, dataset_2 (D2) performed best in both GLCM analysis and deep learning model performance, indicating a strong correlation between the accuracy of classification and the textural qualities that GLCM captured. In the context of plant disease identification, in particular, these results highlight the significance of clearly defined dataset selection criteria in deep learning applications. Scholars can improve the accuracy and dependability of deep learning models for diagnosing plant diseases by giving preference to datasets with favorable GLCM metrics. The research also emphasizes the importance of texture features being taken into account in addition to conventional image features, highlighting the necessity of transparency and rigor in dataset selection procedures.
Keywords
GLCM metrics; deep learning; darkNet19; plant diseases; open datasets; criteria
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.
