Towards Generalizable Crop-Agnostic Plant Disease Recognition: An Unsupervised-Learning Approach
DOI:
https://doi.org/10.47611/jsrhs.v12i4.5472Keywords:
Plant Disease Recognition, Unsupervised Learning, Domain GeneralizationAbstract
In recent years, machine learning techniques have been incorporated in the agricultural industry to automate plant disease recognition. However, most existing frameworks are constrained to the recognition of certain species’ diseases, such as those of tomatoes, due to the severe imbalance in the published dataset. These methods tend to exhibit a strong bias towards specific plants, which require extensive retraining if the model were to accurately classify the disease of other plants. To ensure the system functions in a more realistic context, where images of various plants would be given, it is crucial to develop a generalized system that can recognize a diverse set of plants. To address the aforementioned problem, in this research paper, I propose a novel crop-agnostic plant detection framework. The method leverages contrastive learning, a type of unsupervised learning, to extract discriminative features from plant images regardless of their species. Moreover, the generalizable solution is capable of successfully distinguishing diseases from a dataset with imbalanced class and category. The method achieved an accuracy of 85.88% in the plant village dataset. Through experimental results, it has been demonstrated that the proposed method outperforms existing state-of-the-art methods by a significant margin.
Downloads
References or Bibliography
Abbas, A., Jain, S., Gour, M., & Vankudothu, S. (2021). Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and Electronics in Agriculture, 187, 106279. https://doi.org/10.1016/j.compag.2021.106279
Arora, R. K., Sharma, S., & Singh, B. P. (2014). Late blight disease of potato and its management. Potato Journal, 41(1).
Campbell, C. L., & Madden, L. V. (1990). Introduction to plant disease epidemiology. John Wiley & Sons..
Chaerani, R., & Voorrips, R. E. (2006). Tomato early blight (Alternaria solani): the pathogen, genetics, and breeding for resistance. Journal of general plant pathology, 72, 335-347. https://doi.org/10.1007/s10327-006-0299-3
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020, November). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597-1607). PMLR. https://doi.org/10.48550/arXiv.2002.05709
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258). https://doi.org/10.48550/arXiv.1610.02357
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.48550/arXiv.1512.03385
Jones, C. D., Jones, J. B., & Lee, W. S. (2010). Diagnosis of bacterial spot of tomato using spectral signatures. Computers and electronics in agriculture, 74(2), 329-335. https://doi.org/10.1016/j.compag.2010.09.008
Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., & Menaka, R. (2020). Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing, 86, 105933. https://doi.org/10.1016/j.asoc.2019.105933
Kim, S. K., & Ahn, J. G. (2021). Tomato crop diseases classification models using deep CNN-based architectures. Journal of the Korea Academia-Industrial cooperation Society, 22(5), 7-14.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520). https://doi.org/10.48550/arXiv.1801.04381
Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., & Batra, N. (2020). PlantDoc: A dataset for visual plant disease detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (pp. 249-253). https://doi.org/10.1145/3371158.3371196
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). https://doi.org/10.48550/arXiv.1409.4842
Tm, P., Pranathi, A., SaiAshritha, K., Chittaragi, N. B., & Koolagudi, S. G. (2018, August). Tomato leaf disease detection using convolutional neural networks. In 2018 eleventh international conference on contemporary computing (IC3) (pp. 1-5). IEEE. (DOI) https://doi.org/10.1109/IC3.2018.8530532
Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11).
Wang, Q., Zhou, R., & Yan, Y. (2017). A two-stage approach to note-level transcription of a specific piano. Applied Sciences, 7(9), 901. https://doi.org/10.3390/app7090901
Published
How to Cite
Issue
Section
Copyright (c) 2023 Dongmin Choi; Blessina Devakirubai
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
