Plant Disease Detection From Images Using Deep Learning Techniques based on the Internet Of Things
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Abstract
Plant disease identification and evaluation in a timely and accurate manner is crucial for efficient farming operations for crop yield optimization. Employing the most recent advances in technology, specifically the combination of deep learning and the Internet of Things (IoT), this paper offers an efficient approach to identifying plant diseases. We propose a transfer learning-based deep learning classification model which makes use of pre-trained models including CNN, AlexNet, ResNet, InceptionV3, and VGG-16. To provide wider accessibility, high-resolution images of tomato plant leaves displaying disease symptoms are gathered from a dataset and saved in cloud storage using Internet of Things devices. Following the image extraction from the cloud, images are preprocessed using data argumentation, normalization, color space conversion, background removal, and noise removal. Different plant disease classes are classified using the pre-trained models CNN, AlexNet, ResNet, InceptionV3, and VGG-16. The deep learning models' accuracy is increased using the transfer learning technique, which also cuts down on the workout duration. The VGG-16 model outperforms other models in the experiment, recognizing plant illnesses with an astounding accuracy of 93.7% on average, proving the efficacy of the suggested approach. This novel method has the potential to transform the identification of plant diseases and support sustainable farming methods.
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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Tsaftaris, S. A., Minervini, M., & Scharr, H. (2016). Machine learning for plant phenotyping needs image processing. Trends in plant science, 21(12), 989-991.
Panchal, A. V., Patel, S. C., Bagyalakshmi, K., Kumar, P., Khan, I. R., & Soni, M. (2023). Image-based plant diseases detection using deep learning. Materials Today: Proceedings, 80, 3500-3506.
Agarwal, M., Singh, A., Arjaria, S., Sinha, A., & Gupta, S. (2020). ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Computer Science, 167, 293-301.
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.
Kumar, A., & Vani, M. (2019, July). Image based tomato leaf disease detection. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
Mukhopadhyay, S., Paul, M., Pal, R., & De, D. (2021). Tea leaf disease detection using multi-objective image segmentation. Multimedia Tools and Applications, 80, 753-771.
Kamal, K. C., Yin, Z., Li, D., & Wu, Z. (2021). Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ. Agriculture, 11(9), 1-16.
Jung, M., Song, J. S., Shin, A. Y., Choi, B., Go, S., Kwon, S. Y., ... & Kim, Y. M. (2023). Construction of deep learning-based disease detection model in plants. Scientific Reports, 13(1), 7331.
Ahmad, A., Saraswat, D., & El Gamal, A. (2023). A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agricultural Technology, 3, 100083.
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET) (pp. 1-6). IEEE.
O'shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
Padshetty, S., & Ambika. (2023). Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach. Engineering Proceedings, 59(1), 39.
Hu, W. J., Fan, J., Du, Y. X., Li, B. S., Xiong, N., & Bekkering, E. (2020). MDFC–ResNet: an agricultural IoT system to accurately recognize crop diseases, 8, 115287-115298. IEEE
Chen, H. C., Widodo, A. M., Wisnujati, A., Rahaman, M., Lin, J. C. W., Chen, L., & Weng, C. E. (2022). AlexNet convolutional neural network for disease detection and classification of tomato leaf. Electronics, 11(6), 951.
Alatawi, A. A., Alomani, S. M., Alhawiti, N. I., & Ayaz, M. (2022). Plant disease detection using AI based vgg-16 model. International Journal of Advanced Computer Science and Applications, 13(4).
Lambat, R. K. M., Kothari, R., & Mane, M. K. (2022). Plant disease detection using inceptionv3. International Research Journal of Engineering and Technology (IRJET), 9(06).
Saritha, S., Srinivas, V. S., Anuhya, D., & Pavithra, G. (2022). Performance Analysis of Detection of Disease on Leaf Images with Inception V3 and Mobilenet Deep Learning Techniques. Journal of Pharmaceutical Negative Results, 111-117.
Kaggle Plant disease available online : https://www.kaggle.com/datasets/andrewmvd/leaf-type-detection (accessed on 12 January 2024)
Ramesh, S., Hebbar, R., Niveditha, M., Pooja, R., Shashank, N., & Vinod, P. V. (2018, April). Plant disease detection using machine learning. In 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C) (pp. 41-45). IEEE.
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), 468
Sardogan, M., Tuncer, A., & Ozen, Y. (2018, September). Plant leaf disease detection and classification based on CNN with LVQ algorithm. In 2018 3rd international conference on computer science and engineering (UBMK) (pp. 382-385). IEEE.