Abstract
The tomato has a high market value and is one of the vegetables grown in the most significant quantity globally. Tomato plants are susceptible to diseases, which can negatively impact the fruit's yield and quality. Detecting these illnesses at an early stage and their accurate identification is necessary for successfully managing diseases and reducing losses. In recent years, deep learning methods such as convolutional neural networks (CNNs) have demonstrated significant promise in identifying plant diseases from images. This research suggested a CNN-based strategy for detecting tomato leaf diseases using transfer learning. Transfer learning enables us to enhance the performance of our disease detection model using a smaller dataset by leveraging pre-trained CNN models that have been trained on large datasets. The proposed transfer learning model through Resnet50 and Inception V3 is effective by applying it to a dataset of tomato leaf images. As a result, a high level of accuracy is achieved and could be indulged for practical applications in agriculture.
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References
N. K. E., K. M., P. P., A. R., V. S.: Tomato Leaf Disease Detection using Convolutional Neural Network with Data Augmentation. In: 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp. 1125–1132 (2020). https://doi.org/10.1109/ICCES48766.2020.9138030
Kurup, R.V., Anupama, M.A., Vinayakumar, R., Sowmya, V., Soman, K.P.: Capsule Network for Plant Disease and Plant Species Classification. In: Smys, S., Tavares, J.M.R.S., Balas, V.E., Iliyasu, A.M. (eds.) ICCVBIC 2019. AISC, vol. 1108, pp. 413–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37218-7_47
Arunnehru, J., Vidhyasagar, B.S., Anwar Basha, H.: Plant Leaf Diseases Recognition Using Convolutional Neural Network and Transfer Learning. In: Bindhu, V., Chen, J., Tavares, J.M.R.S. (eds.) International Conference on Communication, Computing and Electronics Systems. LNEE, vol. 637, pp. 221–229. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2612-1_21
Salvi, R.S., Labhsetwar, S.R., Kolte, P.A., Venkatesh, V.S., Baretto, A.M.: Predictive analysis of diabetic retinopathy with transfer learning. In: 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), pp. 1–6. IEEE (2021)
Jebadas, D.G., Sivaram, M., M, A., Vidhyasagar, B.S., Kannan, B.B.: Histogram Distance Metric Learning to Diagnose Breast Cancer using Semantic Analysis and Natural Language Interpretation Methods. In: Johri, P., Diván, M.J., Khanam, R., Marciszack, M., Will, A. (eds.) Trends and Advancements of Image Processing and Its Applications. EICC, pp. 249–259. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-75945-2_13
Xiaoling X., Xu, C., Nan, B.: Inception-v3 for flower classification. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 783–787 (2017)https://doi.org/10.1109/ICIVC.2017.7984661
Kumar, R., Singh, D., Chug, A., Singh, A.P.: Evaluation of Deep learning based Resnet50 for Plant Disease Classification with Stability Analysis. In: 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 1280–1287 (2022). https://doi.org/10.1109/ICICCS53718.2022.9788207
Nawaz, M., Nazir, T., Javed, A., et al.: A robust deep learning approach for tomato plant leaf disease localization and classification. Sci. Rep. 12, 18568 (2022). https://doi.org/10.1038/s41598-022-21498-5
Chowdhury, E.H., et al.: Tomato leaf diseases detection using deep learning technique. Technol. Agric. 453 (2021)
Belal A.M.A, Abu-Naser. S.S.: Image-based tomato leaves diseases detection using deep learning (2018)
Mosin, H., Tanawala, B., Patel, K.J.: Deep learning precision farming: Tomato leaf disease detection by transfer learning. In: Proceedings of 2nd international conference on advanced computing and software engineering (ICACSE) (2019)
Kumari, CU., Jeevan P.S., Mounika, G.: Leaf disease detection: feature extraction with K-means clustering and classification with ANN. In: 2019 3rd international conference on computing methodologies and communication (ICCMC). IEEE (2019)
Qiang, Z., He, L., Dai, F.: Identification of Plant Leaf Diseases Based on Inception V3 Transfer Learning and Fine-Tuning. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, K.-K. (eds.) iSCI 2019. CCIS, vol. 1122, pp. 118–127. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1301-5_10
Serawork, W., Polceanu, M., Buche, C.: Soybean plant disease identification using convolutional neural network. In: FLAIRS conference (2018)
Hasan, M.M.M., et al.: An efficient disease detection technique of rice leaf using AlexNet. J. Comput. Commun. 8(12), 49 (2020)
Gunjan, C., et al.: Potato leaf disease detection using inception V3. Int. Res. J. Eng. Technol (IRJET) 7(11), 1363–1366 (2020)
Mitra, A., Mohanty, S.P., Kougianos, E.: aGROdet: a Novel framework for plant disease detection and leaf damage estimation. In: Proceedings of the IFIP International Internet of Things Conference (IFIP-IoT), pp. 3–22 (2022)
Mitra, A., Mohanty, S.P., Kougianos, E.: A smart agriculture framework to automatically track the spread of plant diseases using mask region-based convolutional neural network. In: Proceedings of the IFIP International Internet of Things Conference (IFIP-IoT), pp. 68–85 (2022)
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Vidhyasagar, B.S., Harshagnan, K., Diviya, M., Kalimuthu, S. (2024). Prediction of Tomato Leaf Disease Plying Transfer Learning Models. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-031-45878-1_20
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