Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks
DOI:
https://doi.org/10.4108/eetsis.3937Keywords:
Potato leaf diseases, Convolutional Neural Networks, VGG19, DenseNet121, ResNet50, Disease recognition, Disease prediction, Data augmentation, Model comparison, Precision agriculture, Disease management, Computational efficiency, Performance evaluation, DLAbstract
Potato crops are vital to global food security and economy, yet they are vulnerable to a wide range of leaf diseases that can significantly impact yield and quality. Rapid diagnosis and accurate identification of these disorders are critical for effective disease control and prevention. In this research, we offer an extensive evaluation and contrast of three state -of-art CNN models- VGG19, DenseNet121 and ResNet50-in order to identify and forecast potato leaf diseases. Our study employed a sizable dataset of potato leaf images, containing diverse healthy and afflicted specimens, to train and assess the performance of the chosen CNN models. Extensive data augmentation techniques were employed to enhance the dataset’s diversity and generalization capabilities. We evaluated the models considering their accuracy, precision, recall, F1-score and computational efficiency to determine the most fitting model for real-life applications. The results demonstrate that all three CNN models achieved high performance in identifying and predicting potato leaf diseases, with VGG19 emerging as the top performer followed closely by DenseNet121 and ResNet50.Our findings provide valuable insights into the efficacy of DL approaches for potato leaf ailment identification and offer a foundation for future research and deployment of these models in precision agriculture systems. Ultimately, this work aims to support the development of more robust and efficient tools for timely disease diagnosis, enabling farmers and agronomists to make better-informed decisions and safeguard the health and productivity of potato crops worldwide.
References
Mekouar, M. A. (2023). 15. Food and Agriculture Organization of the United Nations (FAO). Yearbook of International Environmental Law, 32(1), 298–304. https://doi.org/10.1093/yiel/yvac040
Fry, W. (2008). Phytophthora infestans: the plant (and R gene) destroyer. Molecular Plant Pathology, 9(3), 385–402. https://doi.org/10.1111/j.1364-3703.2007.00465.x
Pethybridge, S. J., & Nelson, S. C. (2015). Leaf Doctor: A New Portable Application for Quantifying Plant Disease Severity. Plant Disease, 99(10), 1310–1316. https://doi.org/10.1094/PDIS-03-15-0319-RE
Barbedo, J. G. A. (2018). Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture, 153, 46–53. https://doi.org/10.1016/j.compag.2018.08.013
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419–1419. https://doi.org/10.3389/fpls.2016.01419
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep Learning for Image-Based Cassava Disease Detection. Frontiers in Plant Science, 8, 1852–1852. https://doi.org/10.3389/fpls.2017.01852
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272–279. https://doi.org/10.1016/j.compag.2018.03.032
Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. https://doi.org/10.48550/arxiv.1409.1556
Huang, G., Liu, Z., Laurens van der Maaten, & Weinberger, K. Q. (2018). Densely Connected Convolutional Networks. arXiv.org.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Ghosal, S., Blystone, D., Singh, A. K., Ganapathysubramanian, B., Singh, A., & Sarkar, S. (2018). An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences - PNAS, 115(18), 4613–4618. https://doi.org/10.1073/pnas.1716999115
Lu, J., Hu, J., Zhao, G., Mei, F., & Zhang, C. (2017). An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture, 142, 369–379.https://doi.org/10.1016/j.compag.2017.09.012
Rizqi, H. D., & Purnomo, A. S. (2017). The ability of brown-rot fungus Daedalea dickinsii to decolorize and transform methylene blue dye. World Journal of Microbiology & Biotechnology, 33(5), 92. https://doi.org/10.1007/s11274-017-2256-z
Chen, J., Deng, X., Wen, Y., Chen, W., Zeb, A., & Zhang, D. (2022). Weakly-supervised learning method for the recognition of potato leaf diseases. The Artificial Intelligence Review, 1–18. https://doi.org/10.1007/s10462-022-10374-3
Lee, T.-Y., Lin, I.-A., Yu, J.-Y., Yang, J., & Chang, Y.-C. (2021). High Efficiency Disease Detection for Potato Leaf with Convolutional Neural Network. SN Computer Science, 2(4). https://doi.org/10.1007/s42979-021-00691-9
Rashid, M., Li, Y. Y., Wang, Y., & Han, C. G. (2019). First Report of Potato virus H Infecting Potatoes in Bangladesh. Plant Disease, 103(5), 1051–1051. https://doi.org/10.1094/PDIS-11-18-1932-PDN
Saeed, I. A. ., MacGuidwin, A. ., Rouse, D. ., & Malek, C. (2007). Field Study on the Influence of Verticillium dahliae and Pratylenchus penetrans on Gas Exchange of Potato. Plant Disease, 91(12), 1531–1535. https://doi.org/10.1094/PDIS-91-12-1531
Kumar, R., Kaundal, P., Tiwari, R. K., Lal, M. K., Kumari, H., Kumar, R., Naga, K. C., Kumar, A., Singh, B., Sagar, V., & Sharma, S. (2023). Development of Reverse Transcription Recombinase Polymerase Amplification (RT-RPA): A Methodology for Quick Diagnosis of Potato Leafroll Viral Disease in Potato. International Journal of Molecular Sciences, 24(3), 2511. https://doi.org/10.3390/ijms24032511
El-Sayed, S. F., Hassan, H. A., & El-Mogy, M. M. (2015). Impact of Bio- and Organic Fertilizers on Potato Yield, Quality and Tuber Weight Loss After Harvest. Potato Research, 58(1), 67–81. https://doi.org/10.1007/s11540-014-9272-2
Blacket, M. J., Agarwal, A., Wainer, J., Triska, M. D., Renton, M., & Edwards, J. (2019). Molecular Assessment of the Introduction and Spread of Potato Cyst Nematode, Globodera rostochiensis, in Victoria, Australia. Phytopathology, 109(4), 659–669. https://doi.org/10.1094/PHYTO-06-18-0206-R
Iqbal, M. A., & Talukder, K. H. (2020). Detection of Potato Disease Using Image Segmentation and Machine Learning. 2020 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 43–47. https://doi.org/10.1109/WiSPNET48689.2020.9198563
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature (London), 521(7553), 436–444. https://doi.org/10.1038/nature14539
Herawan, T., Ghazali, R., Nawi, N. M., & Deris, M. M. (2016). Text Detection in Low Resolution Scene Images Using Convolutional Neural Network. In Recent Advances on Soft Computing and Data Mining (Vol. 549, pp. 366–375). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-51281-5_37
Afakh, M. L., Risnumawan, A., Anggraeni, M. E., Tamara, M. N., & Ningrum, E. S. (2017). Aksara jawa text detection in scene images using convolutional neural network. 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 77–82. https://doi.org/10.1109/KCIC.2017.8228567
Sulistijono, I. A., & Risnumawan, A. (2016). From concrete to abstract: Multilayer neural networks for disaster victims detection. 2016 International Electronics Symposium (IES), 93–98. https://doi.org/10.1109/ELECSYM.2016.7860982
Sulistijono, I. A., Imansyah, T., Muhajir, M., Sutoyo, E., Anwar, M. K., Satriyanto, E., Basuki, A., & Risnumawan, A. (2018). Implementation of Victims Detection Framework on Post Disaster Scenario. 2018 International Electronics Symposium on Engineering Technology and Applications (IES-ETA), 253–259. https://doi.org/10.1109/ELECSYM.2018.8615503
Anwar, M. K., Muhajir, M., Sutoyo, E., Afakh, M. L., Risnumawan, A., Purnomo, D. S., Suryawati Ningrum, E., Darojah, Z., Darmawan, A., & Tamara, M. N. (2018). Deep Features Representation for Automatic Targeting System of Gun Turret. 2018 International Electronics Symposium on Engineering Technology and Applications (IES-ETA), 107–112. https://doi.org/10.1109/ELECSYM.2018.8615541
Anwar, M. K., Muhajir, M., Sutoyo, E., Afakh, M. L., Risnumawan, A., Purnomo, D. S., Suryawati Ningrum, E., Darojah, Z., Darmawan, A., & Tamara, M. N. (2018). Deep Features Representation for Automatic Targeting System of Gun Turret. 2018 International Electronics Symposium on Engineering Technology and Applications (IES-ETA), 107–112. https://doi.org/10.1109/ELECSYM.2018.8615541
Imaduddin, H., Anwar, M. K., Perdana, M. I., Sulistijono, I. A., & Risnumawan, A. (2018). Indonesian Vehicle License Plate Number Detection Using Deep Convolutional Neural Network. 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 158–163. https://doi.org/10.1109/KCIC.2018.8628488
Dinama, D. M., A'yun, Q., Syahroni, A. D., Adji Sulistijono, I., & Risnumawan, A. (2019). Human Detection and Tracking on Surveillance Video Footage Using Convolutional Neural Networks. 2019 International Electronics Symposium (IES), 534–538. https://doi.org/10.1109/ELECSYM.2019.8901603
A Novel Approach for Diabetic Retinopathy Screening Using Asymmetric Deep Learning Feature, Pradeep Kumar Jena, Bonomali Khuntia, Charulata Palai, Manjushree Nayak, Tapas Kumar Mishra, Sachi Nandan Mohanty, Big Data Cognitive Computing (2023), Vol 7, Issue 1, 25, https://doi.org/10.3390/bdcc7010025, ISSN: 2504-2289
Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images, Sara A Althubiti, Sanchita Paul, Rajnikanta Mohanty, Sachi Nandan Mohanty, Fayadh Alenezi, Kemal Polat, Computational and Mathematical Methods in Medicine (Hindawi), 2022, doi.org/10.1155/2022/2733965
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Hritwik Ghosh, Irfan Sadiq Rahat, Kareemulla Shaik, Syed Khasim, Manava Yesubabu
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
