Abstract
Plant diseases have to be properly identified and classified in advance for us to expect our agricultural harvest protection system to function optimally. Rice is the staple food for many Asian countries and is very important in maintaining not only dietary but also socio-economic stability in many countries across the world. In this particular study, we have focused mainly on various diseases affecting rice variants and their detection. To properly identify and treat the variants of diseases affecting rice plants, we first gather visual data, and then we have classified these image data into three diseased categories which are Leaf Blast, Brown Spot, and Healthy leaf. Our dataset consists of a total of 3355 images of healthy and diseased rice leaf images. Now to further solve this problem, we have used a technique called Transfer Learning. Here, we made use of a model which was pre-trained named Xception for training our model. Based on the depthwise separable convolution layer, this is a convolutional neural network architecture. It is conceptually similar to Inception. However, it outperforms Inception V3 as its model parameters are used in a more efficient manner as compared to Inception. By following this methodology, we have obtained encouraging results and we could use this technique for the early detection of diseases in rice varieties. On further improvement, proper implementation of this technique could be attainable in real-life agricultural fields.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gupta T (2017) Plant leaf disease analysis using image processing technique with modified SVM-CS classifier. Int J Eng Manag Technol 5:11–17
Mishra S, Mallick PK, Tripathy HK, Jena L, Chae G-S (2021) Stacked KNN with hard voting predictive approach to assist hiring process in IT organizations. Int J Electr Eng Educ. https://doi.org/10.1177/0020720921989015
Liu L, Zhou G (2009) Extraction of the rice leaf disease image based on BP neural network. In: International conference on computational intelligence and software engineering, pp 1–3
Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8(11):468
Hu YH, Ping XW, Xu MZ, Dan WX, He Y (2016) Detection of late blight disease on potato leaves using hyperspectral imaging technique. Spectrosc Spect Anal 36(2):515–519
Bhattacharya S, Mukherjee A, Phadikar S (2020) A deep learning approach for the classifification of rice leaf diseases. In: Bhattacharyya S, Mitra S, Dutta P (eds) Intelligence enabled research. AISC, vol 1109, pp 61–69. Springer, Singapore. https://doi.org/10.1007/978-981-15-2021-1_8
Toda Y et al (2019) How convolutional neural networks diagnose plant disease. Plant Phenomics 2019:9237136
Liang WJ, Zhang H, Zhang GF, Cao HX (2019) Rice blast disease recognition using a deep convolutional neural network. Sci Rep 9(1):1–10
Jeon WS, Rhee SY (2017) Plant leaf recognition using a convolution neural network. Int J Fuzzy Logic Intell Syst 17(1):26–34
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identifification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384
Khan MA et al (2018) CCDF: automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Comput Electron Agric 155:220–236
Kaur R, Kaur V (2018) A deterministic approach for disease prediction in plants using deep learning, vol 7
Tm P, Pranathi A, SaiAshritha K, Chittaragi NB, Koolagudi SG (2018) Tomato leaf disease detection using convolutional neural networks. In: International conference on contemporary computing (IC3), pp 1–5. IEEE
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS, pp 1097–1105
LeCun Y et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551
Szegedy C et al (2015) Going deeper with convolutions. In: IEEE CVPR, pp 1–9
Rahman CR et al (2020) Identification and recognition of rice diseases and pests using convolutional neural networks. Biosyst Eng 194:112–120
Rajmohan R, Pajany M, Rajesh R, Raman DR, Prabu U (2018) Smart paddy crop disease identification and management using deep convolution neural network and SVM classifier. Int J Pure Appl Math 118(15):255–264
Mohanty SP, Hughes DP, Salathe M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7, 1419
Mallick PK, Mishra S, Chae GS (2020) Digital media news categorization using Bernoulli document model for web content convergence. Pers Ubiquitous Comput 1–16
Jena L, Kamila NK, Mishra S (2014) Privacy preserving distributed data mining with evolutionary computing. In: Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA) 2013, pp 259–267. Springer, Cham
Mishra S, Mallick PK, Jena L, Chae G-S (2020) Optimization of skewed data using sampling-based preprocessing approach. Front Public Heal 8:274
Mishra S, Mallick PK, Tripathy HK, Bhoi AK, González-Briones A (2020) Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl Sci 10(22):8137
Patidar S, Pandey A, Shirish BA, Sriram A (2020) Rice plant disease detection and classifification using deep residual learning. In: Bhattacharjee A, Borgohain SK, Soni B, Verma G, Gao X-Z (eds) MIND 2020, CCIS, vol 1240, pp 278–293. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7-23
Mishra S, Tripathy HK, Mishra BK (2018) Implementation of biologically motivated optimisation approach for tumour categorisation. Int J Comput Aided Eng Technol 10:244–256
Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing, vol. 77, pp 485–494. Singapore, Springer
Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2021) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing, pp 485–494. Springer, Singapore
Roy SN, Mishra S, Yusof SM (2021) Emergence of drug discovery in machine learning. Tech Adv Mach Learn Healthcare 119
Tutica L, Vineel KSK, Mishra S, Mishra MK, Suman S (2021) Invoice deduction classification using LGBM prediction model. In: Advances in electronics, communication and computing, pp 127–137. Springer, Singapore
Mishra S, Chaudhury P, Mishra BK, Tripathy HK (2016) An implementation of feature ranking using machine learning techniques for diabetes disease prediction. In: Proceedings of the second international conference on information and communication technology for competitive strategies, pp 1–3
Rath M, Mishra S (2019) Advanced-level security in network and real-time applications using machine learning approaches. In: Machine learning and cognitive science applications in cyber security, pp 84–104. IGI Global
Mishra S, Sahoo S, Mishra BK (2019) Addressing security issues and standards in Internet of things. In: Emerging trends and applications in cognitive computing, pp 224–257. IGI Global
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sinha, K., Ghoshal, D., Bhunia, N. (2022). Rice Leaf Disease Classification Using Transfer Learning. In: Mallick, P.K., Bhoi, A.K., Barsocchi, P., de Albuquerque, V.H.C. (eds) Cognitive Informatics and Soft Computing. Lecture Notes in Networks and Systems, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-16-8763-1_38
Download citation
DOI: https://doi.org/10.1007/978-981-16-8763-1_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8762-4
Online ISBN: 978-981-16-8763-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)