TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING
Mahmoud BAKR
mah.bakr.2005@gmail.comClimate Change Information Center and Expert Systems, Agricultural Research Center, (Egypt)
Sayed ABDEL-GABER
Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, (Egypt)
Mona NASR
Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, (Egypt)
Maryam HAZMAN
Climate Change Information Center and Expert Systems, Agricultural Research Center (Egypt)
Abstract
Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
Keywords:
Leaf Disease Detection, Convolutional Neural Network, Deep Learning, Transfer LearningReferences
Afifi, A., Alhumam, A., & Abdelwahab, A. (2021). Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data. Plants, 10(1), 28. https://doi.org/10.3390/plants10010028
DOI: https://doi.org/10.3390/plants10010028
Google Scholar
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. https://doi.org/10.1016/j.procs.2020.03.225
DOI: https://doi.org/10.1016/j.procs.2020.03.225
Google Scholar
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for imagebased plant disease identification. Computers and Electronics in Agriculture, 173, 105393. https://doi.org/10.1016/j.compag.2020.105393
DOI: https://doi.org/10.1016/j.compag.2020.105393
Google Scholar
Gulli, A., & Pal, S. (2017). Deep Learning with Keras. Packt.
Google Scholar
Hong, H., Lin, J., & Huang, F. (2020). Tomato Disease Detection and Classification by Deep Learning. In 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) (pp. 25–29). IEEE. https://doi.org/10.1109/ICBAIE49996.2020.00012
DOI: https://doi.org/10.1109/ICBAIE49996.2020.00012
Google Scholar
Huang, G., Liu, Z., & Weinberger, K. Q. (2016). Densely Connected Convolutional Networks. CoRR, abs/1608.06993. http://arxiv.org/abs/1608.06993
Google Scholar
Ji, M., Zhang, L., & Wu, Q. (2020). Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Information Processing in Agriculture, 7(3), 418–426. https://doi.org/10.1016/j.inpa.2019.10.003
DOI: https://doi.org/10.1016/j.inpa.2019.10.003
Google Scholar
Jupyter.org. (2021). https://jupyter.org
Google Scholar
Kabir, M. M., Ohi, A. Q., & Mridha, M. F. (2020). A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network. CoRR, abs/2011.05151. https://arxiv.org/abs/2011.05151
Google Scholar
Kaggle. (2018). https://www.kaggle.com/noulam/tomato/download
Google Scholar
Kumar, V., Arora, H., Harsh, & Sisodia, J. (2020). ResNet-based approach for Detection and Classification of Plant Leaf Diseases. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 495– 502). IEEE. https://doi.org/10.1109/ICESC48915.2020.9155585
DOI: https://doi.org/10.1109/ICESC48915.2020.9155585
Google Scholar
Mohamed, A., Abdel-Gaber, S., Nasr, M., & Hazman, M. (2020). An Intelligent Approach to Mitigate Effects of Climate Change and Insects on Crops. International Journal of Computer Science and Information Security (IJCSIS), 18(3), 75–79.
Google Scholar
Peyal, H. I., Shahriar, S. M., Sultana, A., Jahan, I., & Mondol, Md. H. (2021). Detection of Tomato Leaf Diseases Using Transfer Learning Architectures: A Comparative Analysis. In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) (pp. 1–6). IEEE. https://doi.org/10.1109/ACMI53878.2021.9528199
DOI: https://doi.org/10.1109/ACMI53878.2021.9528199
Google Scholar
Plant health and food security. (2017). FAO. http://www.fao.org/3/a-i7829e.pdf
Google Scholar
Rangarajan, A. K., Purushothaman, R., & Ramesh, A. (2018). Tomato crop disease classification using pretrained deep learning algorithm. Procedia Computer Science, 133, 1040–1047. https://doi.org/10.1016/j.procs.2018.07.070
DOI: https://doi.org/10.1016/j.procs.2018.07.070
Google Scholar
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant Disease Detection and Classification by Deep Learning. Plants, 8(11), 468. https://doi.org/10.3390/plants8110468
DOI: https://doi.org/10.3390/plants8110468
Google Scholar
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 60. https://doi.org/10.1186/s40537-019-0197-0
DOI: https://doi.org/10.1186/s40537-019-0197-0
Google Scholar
Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition.
Google Scholar
In Y. Bengio & Y. LeCun (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. http://arxiv.org/abs/1409.1556
Google Scholar
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2818–2826). IEEE. https://doi.org/10.1109/CVPR.2016.308
DOI: https://doi.org/10.1109/CVPR.2016.308
Google Scholar
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272–279. https://doi.org/10.1016/j.compag.2018.03.032
DOI: https://doi.org/10.1016/j.compag.2018.03.032
Google Scholar
Venkatesh, Nagaraju, Y., Sahana, T. S., Swetha, S., & Hegde, S. U. (2020). Transfer Learning based Convolutional Neural Network Model for Classification of Mango Leaves Infected by Anthracnose. In 2020 IEEE International Conference for Innovation in Technology (INOCON) (pp. 1–7). IEEE. https://doi.org/10.1109/INOCON50539.2020.9298269
DOI: https://doi.org/10.1109/INOCON50539.2020.9298269
Google Scholar
Authors
Mahmoud BAKRmah.bakr.2005@gmail.com
Climate Change Information Center and Expert Systems, Agricultural Research Center, Egypt
Authors
Sayed ABDEL-GABERFaculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
Authors
Mona NASRFaculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
Authors
Maryam HAZMANClimate Change Information Center and Expert Systems, Agricultural Research Center Egypt
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