Bolstering Farmer Resilience Against Potato Blight Through EfficientNet Convolutional Neural Network (CNN) Architecture
Keywords:
CNN Architectures, EfficientNet, Machine Learning, Pattern Recognition, Potato Late Blight, Signal Processing.Abstract
This research presents an innovative approach utilizing the EfficientNet architecture for the early detection of potato late blight, a devastating disease threatening potato crops worldwide. The study aims to harness EfficientNet's capabilities for proactive disease monitoring, facilitating prompt interventions and minimizing crop losses. Through meticulous experimentation and model refinement, the proposed methodology achieves an accuracy rate of 98.07% on the test dataset, surpassing previous benchmarks. This accuracy underscores the efficacy of the EfficientNet model in identifying diseased plants, offering a promising tool for early disease detection in agricultural settings. Additionally, this study implemented and compared three other CNN architectures: VGGNet, ResNet, and InceptionNet. The comparative analysis, based on accuracy, precision, recall, and F1 Score, highlights EfficientNet's superior performance. EfficientNet's balanced scaling and parameter efficiency contribute to its high accuracy, while the other architectures demonstrated limitations due to higher parameter counts and architectural complexity. By advancing potato late blight detection, this research contributes to improved crop management practices, enhanced food security, and resilient agricultural systems. The comprehensive evaluation and comparison of these CNN architectures provide valuable insights for future research and practical applications in agricultural disease detection.
Downloads
References
Anim-Ayeko, A. O., Schillaci, C. &Lipani, A. Automatic blight disease detection in potato (Solanumtuberosum L.) and tomato (Solanumlycopersicum, L. 1753) plants using deep learning. Smart Agric. Technol. 4, 100178 (2023).
Islam, F., Hoq, M. N. & Rahman, C. M. Application of Transfer Learning to Detect Potato Disease from Leaf Image. 2019 IEEE Int. Conf. Robot. Autom. Artif. Internet-of-Things 127–130 (2019) doi:10.1109/RAAICON48939.2019.53.
Rashid, J. et al. Multi-Level Deep Learning Model for Potato Leaf Disease Recognition. Electronics 10, (2021).
Al-Adhaileh, M., Verma, A., Aldhyani, T. &Koundal, D. Potato Blight Detection Using Fine-Tuned CNN Architecture. Mathematics 11, 1516 (2023).
Durai, S. G., Sujithra, T. & Iqbal, M. M. Image Classification for Potato Plant Leaf Disease Detection using Deep Learning. in Proceedings of the International Conference on Systems, Control, and Smart Structures (ICSCSS) 154–158 (2023). doi:10.1109/ICSCSS57650.2023.10169446.
Chen, J., Liu, Q. & Gao, L. Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model. Symmetry (Basel). 11, 343- (2019).
Verma, R., Mishra, R. & Pooja. CNN based Leaves Disease Detection in Potato Plant. in Proceedings of the International Symposium on Computing and Networking (ISCON) 1–5 (2023). doi:10.1109/ISCON57294.2023.10112080.
Khobragade, P. A. &Shinde, S. A. Potato Leaf Disease Detection Using CNN. in Proceedings of the International Conference on Smart Generations (SMARTGENCON) 1–5 (2022). doi:10.1109/SMARTGENCON56628.2022.10083986.
Mahum, R. et al. A novel framework for potato leaf disease detection using an efficient deep learning model. Hum. Ecol. Risk Assess. An Int. J. 29, 303–326 (2022).
Islam, M., Dinh, A., Wahid, K. &Bhowmik, P. Detection of potato diseases using image segmentation and multiclass support vector machine. 2017 IEEE 30th Can. Conf. Electr. Comput. Eng. 1–4 (2017) doi:10.1109/CCECE.2017.7946594.
Tan, M. & Le, Q. V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. (2020).
Tan, M. & Le, Q. V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ArXiv abs/1905.1, (2019).
Yousfi, Y., Butora, J., Fridrich, J. & Tsang, C. F. Improving EfficientNet for JPEG Steganalysis. Proc. 2021 ACM Work. Inf. Hiding Multimed. Secur. (2021) doi:10.1145/3437880.3460397.
Cai, H., Chen, T., Zhang, W., Yu, Y. & Wang, J. Efficient Architecture Search by Network Transformation. 2787–2794 (2017) doi:10.1609/aaai.v32i1.11709.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
