Version 1
: Received: 5 July 2024 / Approved: 5 July 2024 / Online: 8 July 2024 (10:07:38 CEST)
How to cite:
AKINCI, T. C.; Ekici, S.; Kabir, M.; Martinez-Morales, A. A. HybridPlantNet23: A Scientific Insight into the Power of Ensemble Modelling using VGG16 and Darknet53 for Plant Disease Classification. Preprints2024, 2024070556. https://doi.org/10.20944/preprints202407.0556.v1
AKINCI, T. C.; Ekici, S.; Kabir, M.; Martinez-Morales, A. A. HybridPlantNet23: A Scientific Insight into the Power of Ensemble Modelling using VGG16 and Darknet53 for Plant Disease Classification. Preprints 2024, 2024070556. https://doi.org/10.20944/preprints202407.0556.v1
AKINCI, T. C.; Ekici, S.; Kabir, M.; Martinez-Morales, A. A. HybridPlantNet23: A Scientific Insight into the Power of Ensemble Modelling using VGG16 and Darknet53 for Plant Disease Classification. Preprints2024, 2024070556. https://doi.org/10.20944/preprints202407.0556.v1
APA Style
AKINCI, T. C., Ekici, S., Kabir, M., & Martinez-Morales, A. A. (2024). HybridPlantNet23: A Scientific Insight into the Power of Ensemble Modelling using VGG16 and Darknet53 for Plant Disease Classification. Preprints. https://doi.org/10.20944/preprints202407.0556.v1
Chicago/Turabian Style
AKINCI, T. C., Masud Kabir and Alfredo A. Martinez-Morales. 2024 "HybridPlantNet23: A Scientific Insight into the Power of Ensemble Modelling using VGG16 and Darknet53 for Plant Disease Classification" Preprints. https://doi.org/10.20944/preprints202407.0556.v1
Abstract
This study evaluates the effectiveness of Darknet53 and VGG16 pretrained models to develop a hybrid ensemble model named HybridPlantNet23 deep learning model for effective plant disease classification, using the publicly available dataset ‘A Dataset of Leaf Images’ from Mendeley webpage. The preprocessing of the dataset involved resizing the images to the respective requirements of the pretrained models to develop a combined datastore which served as the input. All experiments were conducted using MATLAB R2023b deep network designer interface. Model performances were evaluated based on the conventional accuracy, precision, recall, and F1-score. VGG16 achieved the highest accuracy (97.25%), precision (96.30%), recall (96.96%), and F1-score (96.55%), outperforming both Darknet53 and the ensemble HybridPlantNet23 model. Despite the advanced architecture of the ensemble model, its performance (accuracy: 95.37%, precision: 93.56%, recall: 94.20%, F1-score: 93.74%) was not significantly superior, thus highlighting scientific reasons behind this development. Recommendations for future works were provided accordingly.
Keywords
classification; deep learning; pretrained models; plant diseases; ensemble model
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.
