A Deep Learning Approach For Automated Rice Disease Detection And Classification
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Abstract
Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. Because of this, crops sometimes lose 20% to 40% of their value. A good harvest can depend on early discovery of these diseases, so farmers would need to be able to read and understand images of the diseases. Also, farmers still can't reach their goal of doing daily studies of their huge farmlands. It would be very expensive to do this, so the price of rice for buyers would go up even if it were possible. This paper proposed a pre-trained Deep Convolutional Neural Network (DCNN) method based on optimization for accurately finding and classifying rice leaf disease. It uses both transfer learning and baseline learning. A precise diagnosis method can find and classify eleven different types of rice diseased healthy, leaf blast, brown spot, bacterial blight and bacterial leaf blight, false stump, neck blast, stemborer, tumgro, hispa, and BPH. The most advanced large-scale architecture, such as XceptionNet, ResNet50, DenseNet VGG19, SequeezeNet and CNN used for recognition of the Rice disease, with SGDM, ADAM, RMS propagation optimization methods for predictions for a dataset. The -proposed models were trained and tested using datasets gathered from websites. In the simulation results consistently demonstrate that the XceptionNet model outperforms other architectures in terms of higher accuracy 93.3 %.
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References
Chauhan, B.S.; Jabran, K.; Mahajan, G. Rice Production Worldwide; Springer: Berlin/Heidelberg, Germany, 2017; Volume 247.
Khan, N.; Ray, R.L.; Sargani, G.R.; Ihtisham, M.; Khayyam, M.; Ismail, S. Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability 2021, 13, 4883.
Sharma, A.; Jain, A.; Gupta, P.; Chowdary, V. Machine learning applications for precision agriculture: A comprehensive review. IEEE Access 2020, 9, 4843–4873.
Mathew, A.; Amudha, P.; Sivakumari, S. Deep learning techniques: An overview. In Advanced Machine Learning Technologies and Applications, Proceedings of AMLTA 2020; Springer: Berlin/Heidelberg, Germany, 2021; pp. 599–608.
Lu, Y.; Yi, S.; Zeng, N.; Liu, Y.; Zhang, Y. Identification of rice diseases using deep convolutional neural networks. Neurocomputing 2017, 267, 378–384.
Han, X.; Zhang, Z.; Ding, N.; Gu, Y.; Liu, X.; Huo, Y.; Qiu, J.; Yao, Y.; Zhang, A.; Zhang, L.; et al. Pre-trained models: Past, present and future. AI Open 2021, 2, 225–250.
Eunice, J.; Popescu, D.E.; Chowdary, M.K.; Hemanth, J. Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications. Agronomy 2022, 12, 2395.
Thompson, N.C.; Greenewald, K.; Lee, K.; Manso, G.F. The computational limits of deep learning. arXiv 2020, arXiv:2007.05558.
Hossain, S.M.; Tanjil, M.; Morhsed, M.; Ali, M.A.B.; Islam, M.Z.; Islam, M.; Mobassirin, S.; Sarker, I.H.; Islam, S. Rice leaf diseases recognition using convolutional neural networks. In Advanced Data Mining and Applications, Proceedings of the 16th International Conference, ADMA 2020, Foshan, China, 12–14 November 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 299–314.
Chen, S.; Zhang, K.; Zhao, Y.; Sun, Y.; Ban, W.; Chen, Y.; Zhuang, H.; Zhang, X.; Liu, J.; Yang, T. An approach for rice bacterial leaf streak disease segmentation and disease severity estimation. Agriculture 2021, 11, 420.
Stephen, A.; Punitha, A.; Chandrasekar, A. Designing self attention-based ResNet architecture for rice leaf disease classification. Neural Comput. Appl. 2022, 35, 6737–6751.
Li, D.; Wang, R.; Xie, C.; Liu, L.; Zhang, J.; Li, R.; Wang, F.; Zhou, M.; Liu, W. A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network. Sensors 2020, 20, 578.
Rice Leaf Diseases Dataset. Available online: https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases (accessed on 17 January 2023).
Rice Leaf Disease Image Samples. Available online: https://data.mendeley.com/datasets/fwcj7stb8r/1 (accessed on 17 January 2023).
Nanni, L.; Paci, M.; Brahnam, S.; Lumini, A. Comparison of different image data augmentation approaches. J. Imaging 2021, 7, 254.
Thangaraj, R.; Anandamurugan, S.; Kaliappan, V.K. Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. J. Plant Dis. Prot. 2021, 128, 73–86. Agronomy 2023, 13, 961 24 of 24
Noor, A.; Zhao, Y.; Koubâa, A.; Wu, L.; Khan, R.; Abdalla, F.Y. Automated sheep facial expression classification using deep transfer learning. Comput. Electron. Agric. 2020, 175, 105528.
Kaya, A.; Keceli, A.S.; Catal, C.; Yalic, H.Y.; Temucin, H.; Tekinerdogan, B. Analysis of transfer learning for deep neural network based plant classification models. Comput. Electron. Agric. 2019, 158, 20–29.
Artzai Picona,, Maximiliam Seitzc,d, Aitor Alvarez-Gilaa, Patrick Mohnked, Amaia Ortiz-Barredob,Jone Echazarraa Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification, TECNALIA, Parque Tecnolgico de Bizkaia, C/ Geldo. Edificio 700, E-48160 Derio, Bizkaia, Spain Elsevier 201
Parul Sharmaa, Yash Paul Singh Berwal , Wiqas Ghai Performance analysis of deep learning CNN modelsfor disease detection in plants using image segmentations RIMT University, Mandi Gobindgarh 147301, , Haryana, India Elsevier 2018
Karthik R. Hariharan M. Sundar Anand , Priyanka Mathikshara , Annie Johnson , Menaka R. Attention embedded residual CNN for disease detection in tomato leaves hennai, India 1568-4946/ 2019 Elsevier
S. Ramesh , D. Vydeki Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm 25 March Elsevier 201
Barbedo, J., 2016. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 144, 52–60.
Barbedo, J., 2019. Plant disease identification from individual lesions and spots using deep learning. Biosyst. Eng. 180, 96–107.
Singh, J., Kaur, H., 2018. Plant disease detection based on region-based segmentation and KNN classifier. In: International Conference on ISMAC in Computational Vision and Bio-Engineering. Springer, Cham, pp. 1667–1675.
Naik, M., Sivappagari, C., 2016. Plant leaf and disease detection by using HSV features and SVM classifier. Int. J. Eng. Sci. 3794.
Chaudhary, A., Kolhe, S., Kamal, R., 2016. An improved random forest classifier for multi-class classification. Inform. Process. Agric. 3 (4), 215–222.
Kamilaris, A., Prenafeta-Boldú, F., 2018. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90.
Li, Y., Chao, X., 2020. ANN-Based Continual Classification in Agriculture. Agriculture 10 (5), 178.
Chen, J., Chen, J., Zhang, D., et al., 2020. Using deep transfer learning for image-based plant disease identification. Comput. Electron. Agric. 173, 105393.
Li, Y., Yang, J., 2020. Few-shot cotton pest recognition and terminal realization. Comput. Electron. Agric. 169, 105240.
Thenmozhi, K., Reddy, U.S., 2019. Crop pest classification based on deep convolutional neural network and transfer learning. Comput. Electron. Agric. 164, 104906.
Too, E., Yujian, L., Njuki, S., et al., 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279.
de Castro, A., Madalozzo, G.A., dos Santos, T.N., et al., 2020. BerryIP embedded: An embedded vision system for strawberry crop. Comput. Electron. Agric. 173, 105354.
Tao, M., Ma, X., Huang, X., et al., 2020. Smartphone-based detection of leaf color levels in rice plants. Comput. Electron. Agric. 173, 105431.
Liakos, K.G., Busato, P., Moshou, D., et al., 2018. Machine learning in agriculture: A review. Sensors 18 (8), 2674.
Liang, X.W., Jiang, A.P., Li, T., et al., 2020. LR-SMOTE–An improved unbalanced data set oversampling based on K-means and SVM. Knowl.-Based Syst. 105845.
Qi Yanga , Liangsheng Shia,⁎ , Jingye Hana , Jin Yua , Kai Huangb A near real-time deep learning approach for detecting rice phenology based on UAV images China https://doi.org/10.1016/j.agrformet.2020.107938 Received 29 July 2019; Received in revised form 30 January 2020; Accepted 9 February 2020
Manoj Agrawal1 , Dr. Shweta Agrawal2 Rice Plant Diseases Detection & Classification Using Deep Learning Models: A Systematic Review Journal Of Critical Reviews Issn- 2394-5125 Vol 7, Issue 11, 2020
Chowdhury R. Rahman, et al. “Identification and Recognition of Rice Diseases and Pests Using Deep Convolutional Neural Networks.” , Biosystems Engineering, published by Elsevier Ltd., vol. 194, 112-120, 2020.
sethy, prabira Kumar (2020), “Rice Leaf Disease Image Samples”, Mendeley Data, V1, doi: 10.17632/fwcj7stb8r.1
Deepika Mandwariya, Dr. Varsha Jotwani (2024), Advanced Techniques for Rice Disease Detection: A Comprehensive Review, IOSR Journal of Computer Engineering (IOSR-JCE); e-ISSN: 2278-0661, Volume 26, Issue 1, Ser. 1 (Jan. – Feb. 2024), pp 31-38. doi: 10.9790/0661-2601013138.