Abstract
Plants are essential for life on earth, providing various resources and are helpful in maintaining ecosystem balance. Plant diseases result in reduced crop productivity and yield. Manual detection and classification of plants diseases is a crucial task. This research presents a hybrid computer aided model for plant disease classification and segmentation. In this research work we have utilized PlantVillage dataset with 8 classes of plant diseases. The dataset was annotated using a Generative Adversarial Network (GAN), four transfer learning models were used for classification, and a hybrid model is proposed based on the pretrained deep learning models. Instance and semantic segmentation were used for localizing disease areas in plants, using a hybrid algorithm. The use of GAN and transfer learning models, as well as the hybrid approach for classification and segmentation, resulted in a robust and accurate model for plant disease detection and management in agriculture. This research could also serve as a model for other image classification and segmentation tasks in different domains. Proposed hybrid model achieved the promising accuracy of 98.78% as compared to the state-of-the-art techniques.
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References
Abbas, A., Jain, S., Gour, M., Vankudothu, S.: Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput. Electron. Agric. 187, 106279 (2021)
Agarwal, M., Kotecha, A., Deolalikar, A., Kalia, R., Yadav, R.K., Thomas, A.: Deep learning approaches for plant disease detection: a comparative review. In: 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6. IEEE (2023)
Aggarwal, A., Mittal, M., Battineni, G.: Generative adversarial network: an overview of theory and applications. Int. J. Inf. Manag. Data Insights 1(1), 100004 (2021)
Ahmad, A., Saraswat, D., El Gamal, A.: A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agric. Technol. 3, 100083 (2023)
Azim, M.A., Islam, M.K., Rahman, M.M., Jahan, F.: An effective feature extraction method for rice leaf disease classification. Telkomnika (Telecommun. Comput. Electron. Control) 19(2), 463–470 (2021)
Barkau, R.L.: UNET, One-Dimensional Unsteady Flow Through a Full Network of Open Channels: User’s Manual. US Army COE, Hydrologic Engineering Center (1996)
Bharati, P., Pramanik, A.: Deep learning techniques-R-CNN to mask R-CNN: a survey. Comput. Intell. Pattern Recogn. Proc. CIPR 2019, 657–668 (2020)
Bhatt, P., Sarangi, S., Pappula, S.: Comparison of CNN models for application in crop health assessment with participatory sensing. In: 2017 IEEE Global Humanitarian Technology Conference (GHTC), pp. 1–7. IEEE (2017)
Chand, S., Hari, R.: Plant disease identification and suggestion of remedial measures using machine learning. In: 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), pp. 895–901. IEEE (2022)
Daniel, J., Rose, J., Vinnarasi, F., Rajinikanth, V.: VGG-UNet/VGG-SegNet supported automatic segmentation of endoplasmic reticulum network in fluorescence microscopy images. Scanning 2022, 7733860 (2022)
Hughes, D., Salathé, M., et al.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 (2015)
Kamal, K., Yin, Z., Wu, M., Wu, Z.: Depthwise separable convolution architectures for plant disease classification. Comput. Electron. Agric. 165, 104948 (2019)
Kantale, P., Thakare, S.: A review on pomegranate disease classification using machine learning and image segmentation techniques. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 455–460. IEEE (2020)
Kc, K., Yin, Z., Li, D., Wu, Z.: Impacts of background removal on convolutional neural networks for plant disease classification in-situ. Agriculture 11(9), 827 (2021)
Khan, K., Khan, R.U., Albattah, W., Qamar, A.M.: End-to-end semantic leaf segmentation framework for plants disease classification. Complexity 2022, 1168700 (2022)
Kumar, R., Chug, A., Singh, A.P., Singh, D.: A systematic analysis of machine learning and deep learning based approaches for plant leaf disease classification: a review. J. Sens. 2022, 1–13 (2022)
Lu, J., Tan, L., Jiang, H.: Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture 11(8), 707 (2021)
Nazarov, P.A., Baleev, D.N., Ivanova, M.I., Sokolova, L.M., Karakozova, M.V.: Infectious plant diseases: etiology, current status, problems and prospects in plant protection. Acta Naturae 12(3), 46 (2020)
Pokkuluri, K.S., Nedunuri, S.U.D., Devi, U.: Crop disease prediction with convolution neural network (CNN) augmented with cellular automata. Int. Arab J. Inf. Technol. 19(5), 765–773 (2022)
Sarvamangala, D., Kulkarni, R.V.: Convolutional neural networks in medical image understanding: a survey. Evol. Intel. 15(1), 1–22 (2022)
Sembiring, A., Away, Y., Arnia, F., Muharar, R.: Development of concise convolutional neural network for tomato plant disease classification based on leaf images. J. Phys. Conf. Ser. 1845, 012009 (2021). IOP Publishing
Sethy, P.K., Barpanda, N.K., Rath, A.K., Behera, S.K.: Image processing techniques for diagnosing rice plant disease: a survey. Procedia Comput. Sci. 167, 516–530 (2020)
Sharma, P., Berwal, Y.P.S., Ghai, W.: Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Inf. Process. Agric. 7(4), 566–574 (2020)
Shoaib, M., et al.: Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease. Front. Plant Sci. 13, 1031748 (2022)
Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., Batra, N.: PlantDoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp. 249–253 (2020)
Sood, M., Singh, P.K., et al.: Hybrid system for detection and classification of plant disease using qualitative texture features analysis. Procedia Comput. Sci. 167, 1056–1065 (2020)
Swaminathan, A., Varun, C., Kalaivani, S., et al.: Multiple plant leaf disease classification using densenet-121 architecture. Int. J. Electr. Eng. Technol 12, 38–57 (2021)
Xian, T.S., Ngadiran, R.: Plant diseases classification using machine learning. J. Phys. Conf. Ser. 1962, 012024 (2021). IOP Publishing
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Taji, K., Taleb Ahmad, Y., Ghanimi, F. (2024). Plant Disease Classification and Segmentation Using a Hybrid Computer-Aided Model Using GAN and Transfer Learning. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_1
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