Automatic Plant Disease Classification with Unknown Class Rejection using Siamese Networks
DOI:
https://doi.org/10.12928/biste.v6i3.11619Keywords:
Potato Plant Diseases, Siamese Neural Network, Deep Learning, ResNet50, CNNAbstract
Potatoes are one of the horticultural commodities with significant trade value both domestically and internationally. To produce high-quality potatoes, healthy and disease-free potato plants are essential. The most common diseases affecting potato plants are late blight and early blight. These diseases appear randomly in different positions and sizes on potato leaves, resulting in numerous combinations of infected leaves. This study proposes an architecture focused on a similarity-based approach, namely the Siamese Neural Network (SNN). SNN can recognize images by comparing two or more images and categorizing the test image accordingly. Thus, SNN has an advantage over classification-based approaches as it can identify various combinations of disease spots on potato plants using a similarity-based approach. This study is divided into two main scenarios: testing with data categories which were previously seen during the training process (traditional testing) and testing with the addition of new data categories that were not seen during training. In the first scenario, SNN showed better accuracy with an accuracy rate of 98.4%, while in the second scenario, SNN achieved an accuracy of 97.1%. That result suggests that SNN can categorize data very well, even recognizing data which never seen during training. These results offer hope that SNN can recognize more disease spots/patterns on potato plants or even identify new diseases by adding these new diseases to the SNN support set without retraining.
References
A. Granwehr and V. Hofer, “Analysis on Digital Image Processing for Plant Health Monitoring,” Journal of Computing and Natural Science, vol. 1, no. 1, pp. 5–8, Jan. 2021, https://doi.org/10.53759/181X/JCNS202101002.
X. Li, Y. Zhou, J. Liu, L. Wang, J. Zhang, and X. Fan, “The Detection Method of Potato Foliage Diseases in Complex Background Based on Instance Segmentation and Semantic Segmentation,” Front Plant Sci, vol. 13, Jul. 2022, https://doi.org/10.3389/fpls.2022.899754.
N. Upadhyay and N. Gupta, “A Survey on Diseases Detection for Agriculture Crops Using Artificial Intelligence,” 2021 5th International Conference on Information Systems and Computer Networks, ISCON 2021, pp. 1-8, 2021, https://doi.org/10.1109/ISCON52037.2021.9702513.
F. Furizal, A. Ma’arif, A. A. Firdaus, and W. Rahmaniar, “Future Potential of E-Nose Technology: A Review,” International Journal of Robotics and Control Systems, vol. 3, no. 3, pp. 449–469, Jul. 2023, https://doi.org/10.31763/ijrcs.v3i3.1091.
Erlin, I. Fuadi, R. N. Putri, D. Nasien, Gusrianty, and D. Oktarina, “Deep Learning Approaches for Potato Leaf Disease Detection: Evaluating the Efficacy of Convolutional Neural Network Architectures,” Revue d’Intelligence Artificielle, vol. 38, no. 2, pp. 717–727, Apr. 2024, https://doi.org/10.18280/ria.380236.
H. Pattanaik, G. Patnaik, A. Gouda, M. Sahoo, and M. Das, “A Comparative Study of Disease Detection in Potato Plants Using Machine Learning and Deep Learning Methods,” in Data Science and Communication, pp. 159–172, 2024, https://doi.org/10.1007/978-981-99-5435-3_11.
B. M. Joshi and H. Bhavsar, “Plant leaf disease detection and control: A survey,” Journal of Information and Optimization Sciences, vol. 41, no. 2, pp. 475–487, 2020, https://doi.org/10.1080/02522667.2020.1734295.
S. Bangari, P. Rachana, N. Gupta, P. S. Sudi, and K. K. Baniya, “A Survey on Disease Detection of a potato Leaf Using CNN,” in 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 144–149, 2022, https://doi.org/10.1109/ICAIS53314.2022.9742963.
M. D. Fauzi, F. D. Adhinata, N. G. Ramadhan, and N. A. F. Tanjung, “A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 8, no. 2, pp. 298–306, 2022, https://doi.org/10.26555/jiteki.v8i2.23886.
S. M. Kiran and D. N. Chandrappa, “Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms,” Journal of Robotics and Control (JRC), vol. 4, no. 6, pp. 840–848, Nov. 2023, https://doi.org/10.18196/jrc.v4i6.20342.
A. Mousavi, A. H. Sadeghi, A. M. Ghahfarokhi, F. Beheshtinejad, and M. M. Masouleh, “Improving the Recognition Percentage of the Identity Check System by Applying the SVM Method on the Face Image Using Special Faces,” International Journal of Robotics and Control Systems, vol. 3, no. 2, pp. 221–232, Mar. 2023, https://doi.org/10.31763/ijrcs.v3i2.939.
P. Sharma, P. Hans, and S. C. Gupta, “Classification Of Plant Leaf Diseases Using Machine Learning And Image Preprocessing Techniques,” in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 480–484, 2020, https://doi.org/10.1109/Confluence47617.2020.9057889.
W. B. Demilie, “Plant disease detection and classification techniques: a comparative study of the performances,” J Big Data, vol. 11, no. 1, 2024, https://doi.org/10.1186/s40537-023-00863-9.
X. Chen and K. He, “Exploring simple Siamese representation learning,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 15745–15753, 2021, https://doi.org/10.1109/CVPR46437.2021.01549.
R. K. Putra and N. P. Utama, “Enhancing X-ray Baggage Inspection through Similarity-based Prohibited Object Identification,” in 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), pp. 16–21, 2023, https://doi.org/10.1109/ICISPC59567.2023.00012.
M. D. Al Fitra and A. Fadlil, “Detection of Fuel Purity Using the TCS3200 Sensor Using the Euclidean Distance Function,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 3, pp. 312–322, 2023, https://doi.org/10.12928/biste.v5i3.8260.
G. A. F. Alfarisy, O. A. Malik, and O. W. Hong, “Towards open domain-specific recognition using Quad-Channel Self-Attention Reciprocal Point Learning and Autoencoder,” Knowl Based Syst, vol. 284, p. 111261, 2024, https://doi.org/10.1016/j.knosys.2023.111261.
G. A. F. Alfarisy, O. A. Malik, and O. W. Hong, “Quad-Channel Contrastive Prototype Networks for Open-Set Recognition in Domain-Specific Tasks,” IEEE Access, vol. 11, pp. 48578–48592, 2023, https://doi.org/10.1109/ACCESS.2023.3275743.
F. Mohameth, C. Bingcai, and K. A. Sada, “Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village,” Journal of Computer and Communications, vol. 8, no. 6, pp. 10–22, 2020, https://doi.org/10.4236/jcc.2020.86002.
S. Mascarenhas and M. Agarwal, “A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification,” in 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), pp. 96–99, 2021, https://doi.org/10.1109/CENTCON52345.2021.9687944.
B. Koonce, “ResNet 50,” in Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, pp. 63–72, 2021, https://doi.org/10.1007/978-1-4842-6168-2_6.
F. Cahyono, W. Wirawan, and R. Fuad Rachmadi, “Face Recognition System using Facenet Algorithm for Employee Presence,” in 2020 4th International Conference on Vocational Education and Training (ICOVET), pp. 57–62, 2020, https://doi.org/10.1109/ICOVET50258.2020.9229888.
Z. Tan et al., “Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition,” IEEE Transactions on Image Processing, vol. 31, pp. 3224–3235, 2022, https://doi.org/10.1109/TIP.2021.3137005.
J. Li, “Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification,” arXiv preprint arXiv:2408.10193, 2024, https://doi.org/10.48550/arXiv.2408.10193.
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit Lett, vol. 27, no. 8, pp. 861–874, 2006, https://doi.org/10.1016/j.patrec.2005.10.010.
U. Y. Tambe, A. Shobanadevi, A. Shanthini, and H.-C. Hsu, “Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach,” River Publishers Series in Proceedings, 2023, https://doi.org/10.13052/rp-9788770040723.043.
H. Ghosh, I. S. Rahat, K. Shaik, S. Khasim, and M. Yesubabu, “Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks,” ICST Transactions on Scalable Information Systems, vol. 10, no. 6, Sep. 2023, https://doi.org/10.4108/eetsis.3937.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Rizal Kusuma Putra, Gusti Ahmad Fanshuri Alfarisy, Faizal Widya Nugraha, Aninditya Anggari Nuryono
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This journal is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
