From a Proposed CNN Model to a Real-World Application in Rice Disease Classification

• Autorzy: Nguyen, Hoang Long , Tran, Thi Ha , Thi, Hong Ha Le , Nguyen, Dinh Cong
• Języki publikacji: EN

Abstrakty

EN

A compact and precise application of rice disease classification is helpful to assist farmers in their work for treatment on the plants and therefore could be quick and accurate to measure and eliminate the effects of diseases more profitably. In the past, the works were completed by naked-eye observation and basically relied on the experiences. Even so, the results are quite subjective and heuristic. In this paper, a mobile application to automatically classify several kinds of rice diseases from rice plant images and then to accurately recommend the uses of pesticides or chemicals. To do so, a proposed convolutional neural network (CNN) model is given. The results show that the proposed CNN model achieves the performance with the best trade-off between accuracy and time efficiency in comparison with the state-of-the-art models in our dataset. This model could be easily embedded into a mobile application to process in near real-time processing.

Słowa kluczowe

EN

rice diseases; mobile app; CNN; convolutional neural network

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 177--182
• Opis fizyczny: Bibliogr. 22 poz., rys., wykr., tab.

Twórcy

autor

Nguyen, Hoang Long

• Hong Duc University Thanh Hoa, Viet Nam,

autor

Tran, Thi Ha

• HUST Ha Noi, Viet Nam,

autor

Thi, Hong Ha Le

• Hong Duc University Thanh Hoa, Viet Nam,

autor

Nguyen, Dinh Cong

• Hong Duc University Thanh Hoa, Viet Nam,

Bibliografia

• [1] J Kihoro, NJ Bosco, H Murage, E Ateka, D Makihara, “Investigating the impact of rice blast disease on the livelihood of the local farmers in greater Mwea region of Kenya”. Springerplus 2, no. 1: 1-13, 2013
• [2] V. Singh , A.K.Misrab, “Detection of plant leaf diseases using image segmentation and soft computing techniques”, Information Processing in Agriculture, 4, 41-49, 2016
• [3] A. Krizhevsky, .I Sutskever, “Imagenet classification with deep convolutional neural networks”, Advances in neural information processing systems 25, 2012.
• [4] S Sladojevic, M Arsenovic, A Anderla, “Deep neural networks based recognition of plant diseases by leaf image classification”, Computational intelligence and neuroscience, 2016
• [5] Zhao, R., Niu, X., Wu, Y., Luk, W., Liu, Q. “ Optimizing CNN- based object detection algorithms on embedded FPGA platforms”. ISARC (2017).
• [6] H. Cartwright, Ed., Artificial Neural Networks, Humana Press, 2015
• [7] T. Rumpf, A.-K. Mahlein, U. Steiner, E.-C. Oerke, H.-W. Dehne, and L. Plmer, Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance, Computers and Electronics in Agriculture, vol. 74, no. 1, pp. 9199, 2010.
• [8] Md. Rasel Mia, Sujit Roy, Subrata Kumar Das* Mango Leaf Diseases Recognition Using Neural Network and Support Vector Machine
• [9] B. C. Karmokar, M. S. Ullah, Md. K. Siddiquee, and K. Md. R. Alam, Tea leaf diseases recognition using neural network ensemble, International Journal of Computer Applications, vol. 114, no. 17, pp. 2730, 2015.
• [10] I.Guyon and A. Elisseeff , An Introduction to Feature Extraction, Series Studies in Fuzziness and Soft Computing, Physica-Verlag, Springer, 2006.
• [11] S. Ramesh, D. Vydeki, Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm, Information Processing in Agriculture, Vol. 7, Issue 2, 2020, Pages 249-260, ISSN 2214-3173
• [12] Wang, G., Sun, Y., Wang, J. , Automatic image-based plant disease severity estimation using deep learning, Computational intelligence and neuroscience, 2017.
• [13] Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D, Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection, Symmetry, 2019
• [14] Junde Chen, Jinxiu Chen, Defu Zhang, Yuandong Sun, Y.A. Nanehkaran, Using deep transfer learning for image-based plant disease identification, Computers and Electronics in Agriculture, Volume 173, 2020
• [15] B. Mohammed; B.Kamel; M.Abdelouahab, Deep learning for tomato diseases: classification and symptoms visualization, Applied Artificial Intelligence, 2017.
• [16] X. Li and L. Rai, “Apple Leaf Disease Identification and Classification using ResNet Models,” 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), 2020, pp. 738-742
• [17] G. Huang, Z.Liu Densely connected convolutional networks, Proceedings of the IEEE conference on computer vision and pattern recognition, p. 7, 2017.
• [18] R.Chowdhury R., et al. Identification and recognition of rice diseases and pests using convolutional neural networks, Biosystems Engineering, 2020.
• [19] S.Kumar (2020), Rice Leaf Disease Image Samples, Mendeley Data.
• [20] H.Andrew, et al. “Searching for mobilenetv3,” Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.
• [21] SK Upadhyay, “Deep Transfer Learning-Based Rice Leaves Disease Diagnosis and Classification model using InceptionV3”, International Conference on Computational Intelligence and Sustainable Engineering Solutions, pp. 493–499, 2022
• [22] MH Tunio, L Jianping, MHF Butt, “ Identification and Classification of Rice Plant Disease Using Hybrid Transfer Learning”, ICCWAMTIP, pp. 525–529, (2021)

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikatory

DOI

10.15439/2022R34