Abstract
Accurate classification of diseases in potato crops is vital for optimizing yield and ensuring crop health. We propose a generalized framework using Federated Learning (FL) for accurate classification of potato crop diseases. The dataset from Plant Village includes diverse potato leaf images with imbalanced class distributions. By incorporating FL, which enables collaborative model training without sharing raw data, we leverage the collective intelligence of distributed datasets while ensuring privacy. CNN as base model, achieves a 92% classification accuracy in the potato disease dataset through extensive experimentation and hyperparameter fine-tuning. Our approach addresses the challenge of an unbalanced dataset in potato disease classification and contributes to advances in precision agriculture. The framework can be adapted for other crop disease classification tasks, showcasing the potential of distributed learning in agriculture. Overall, our study demonstrates the effectiveness of FL in achieving accurate and scalable disease classification models in potato crops.
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References
Agarwal, M., Gupta, S.K., Biswas, K.K.: Development of efficient CNN model for Tomato crop disease identification. Sustain. Comput. Inform. Syst. 28, 100407 (2020). https://doi.org/10.1016/j.suscom.2020.100407. https://www.sciencedirect.com/science/article/pii/S2210537920301347
Agarwal, M., Gupta, S.K., Biswas, K.: Development of efficient CNN model for tomato crop disease identification. Sustain. Comput. Inform. Syst. 28, 100407 (2020). https://doi.org/10.1016/j.suscom.2020.100407
Agarwal, M., Singh, A., Arjaria, S., Sinha, A., Gupta, S.: ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Comput. Sci. 167, 293–301 (2020). ISBN 1877-0509
Arya, S., Singh, R.: A comparative study of CNN and AlexNet for detection of disease in potato and mango leaf. In: 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, pp. 1–6. IEEE (2019). https://doi.org/10.1109/ICICT46931.2019.8977648. https://ieeexplore.ieee.org/document/8977648/
Eser, S.: A deep learning based approach for the detection of diseases in pepper and potato leaves. Anadolu Tarım Bilimleri Dergisi 36(2), 167–178 (2021)
Gunarathna, M., Rathnayaka, R., Kandegama, W.: Identification of an efficient deep leaning architecture for tomato disease classification using leaf images. J. Food Agric. 13(1), 33 (2020)
Hughes, D.P., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics (2016). https://doi.org/10.48550/arXiv.1511.08060
Iqbal, M.A., Talukder, K.H.: Detection of potato disease using image segmentation and machine learning. In: 2020 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, India, pp. 43–47. IEEE (2020). https://doi.org/10.1109/WiSPNET48689.2020.9198563. https://ieeexplore.ieee.org/document/9198563/
Islam, M., Dinh, A., Wahid, K., Bhowmik, P.: Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4. IEEE (2017)
Jackulin, C., Murugavalli, S.: A comprehensive review on detection of plant disease using machine learning and deep learning approaches. Measur. Sens. 100441 (2022)
Jasim, M.A., AL-Tuwaijari, J.M.: Plant leaf diseases detection and classification using image processing and deep learning techniques. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), Duhok, Iraq, pp. 259–265. IEEE (2020). https://doi.org/10.1109/CSASE48920.2020.9142097. https://ieeexplore.ieee.org/document/9142097/
Javaid, M., Khan, I.H.: Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 pandemic. J. Oral Biol. Craniofacial Res. 11(2), 209–214 (2021). https://doi.org/10.1016/j.jobcr.2021.01.015. https://www.sciencedirect.com/science/article/pii/S2212426821000154
Li, L., Fan, Y., Lin, K.Y.: A survey on federated learning. In: 2020 IEEE 16th International Conference on Control & Automation (ICCA), pp. 791–796. IEEE (2020)
Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50–60 (2020). https://doi.org/10.1109/MSP.2020.2975749
Liu, F., Xiao, Z.: Disease spots identification of potato leaves in hyperspectral based on locally adaptive 1D-CNN. In: 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pp. 355–358. IEEE (2020)
Mammen, P.M.: Federated learning: opportunities and challenges. arXiv preprint arXiv:2101.05428 (2021)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016). ISBN 1664-462X
Oppenheim, D., Shani, G., Erlich, O., Tsror, L.: Using deep learning for image-based potato tuber disease detection. Phytopathology 109(6), 1083–1087 (2019)
Polder, G., Blok, P.M., De Villiers, H.A., Van der Wolf, J.M., Kamp, J.: Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Front. Plant Sci. 10, 209 (2019)
Puspha Annabel, L.S., Annapoorani, T., Deepalakshmi, P.: Machine learning for plant leaf disease detection and classification - a review. In: 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0538–0542 (2019). https://doi.org/10.1109/ICCSP.2019.8698004
Rieke, N., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3(1), 1–7 (2020). https://doi.org/10.1038/s41746-020-00323-1. https://www.nature.com/articles/s41746-020-00323-1
Saba, L., et al.: A multicenter study on carotid ultrasound plaque tissue characterization and classification using six deep artificial intelligence models: a stroke application. IEEE Trans. Instrum. Meas. 70, 1–12 (2021)
Sanagala, S.S., et al.: Ten fast transfer learning models for carotid ultrasound plaque tissue characterization in augmentation framework embedded with heatmaps for stroke risk stratification. Diagnostics 11(11), 2109 (2021)
Sharma, R., Singh, A., Dutta, M.K., Riha, K., Kriz, P., et al.: Image processing based automated identification of late blight disease from leaf images of potato crops. In: 2017 40th International Conference on Telecommunications and Signal Processing (TSP), pp. 758–762. IEEE (2017)
Skandha, S.S., et al.: A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework. Comput. Biol. Med. 141, 105131 (2022)
Suttapakti, U., Bunpeng, A.: Potato leaf disease classification based on distinct color and texture feature extraction. In: 2019 19th International Symposium on Communications and Information Technologies (ISCIT), pp. 82–85. IEEE (2019)
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Sharma, A., Hazara, D., Gupta, S.K., Kushwaha, R., Kumari, D. (2024). Potato Leaf Disease Classification Using Federated Learning. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2026. Springer, Cham. https://doi.org/10.1007/978-3-031-53082-1_16
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