Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review


  • Prameetha Pai, Amutha S, Mustafa Basthikodi, Ananth Prabhu Gurpur, Chaitra K M, Shubhan S Bhat


Rice Leaf Diseases, Deep Learning, Disease Detection, Comparative Analysis, Crop Loss Prevention


Cultivating rice is crucial in India to meet demands of a growing population. In order to improve crop yield, it's essential to address factors like diseases caused by bacteria, fungi, and viruses. Detecting and managing these diseases is vital, and one effective approach is employing rice plant disease detection methods. Deep learning techniques, known for their ability to analyse data, are used for disease identification in plants. This work explores various deep learning approaches for detecting rice plant disease. Deep learning, particularly in computer vision, has shown significant progress in detecting plant diseases. The study compares the effectiveness deep learning mechanisms, demonstrating superior performance of deep learning models. Utilizing deep learning can help prevent major crop losses by detecting leaf diseases through image analysis.


Download data is not yet available.


Mohidem, N.A., Hashim, N., Shamsudin, R., Che Man, H. Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process, and Nutrient Content. Agriculture, 2020, 12, 741.

Gomiero T, Soil Degradation, Land Scarcity, and Food Security: Reviewing a Complex Challenge. Sustainability, 2016, 8, 281. 10.3390/su8030281

Asibi, A.E., Chai, Q., Coulter, J.A, Rice Blast: A Disease with Implications for Global Food Security. Agronomy, 2019, 9, 451.

Yang, H., Deng, X., Shen, H., Lei, Q., Zhang, S., Liu, N, Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer. Agriculture, 2023, 13, 1361.

Latif, G., Abdelhamid, S.E., Mallouhy, R.E., Alghazo, J., & Kazimi, Z.A, Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model. Plants, 2022, 11, 2230. 10.3390/plants 11172230

Raj Kumar, Anuradha Chug, Amit Prakash Singh, & Dinesh Singh, A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review. Journal of Sensors-Hindawi, 2022,

Ferentinos, K.P, Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 2018, 145, 311–318.

Zarbafi, S.S., Ham, J.H, An Overview of Rice QTLs Associated with Disease Resistance to Three Major Rice Diseases: Blast, Sheath Blight, and Bacterial Panicle Blight. Agronomy, 2019, 9, 177.

Garima Pal, Devashish Mehta, Saurabh Singh, Foliar Application or Seed Priming of Cholic Acid-Glycine Conjugates can Mitigate/Prevent the Rice Bacterial Leaf Blight Disease via Activating Plant Defense Genes. Front. Plant Sci., Sec. Plant Biotechnology, 2021, Volume 12. 10.3389/fpls.2021.746912

Simhadri, C.G., Kondaveeti, H.K. Automatic Recognition of Rice Leaf Diseases Using Transfer Learning. Agronomy, 2023, 13, 961.

J., A., 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.

Tugrul, B., Elfatimi, E., Eryigit, R, Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review. Agriculture, 2022, 12, 1192.

Aggarwal, M., Khullar, V., Goyal, N., Singh, A., Tolba, A., Thompson, E.B., & Kumar, S, Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification. Agriculture, 2023, 13, 936.

Shorten, C., Khoshgoftaar, T.M, A survey on Image Data Augmentation for Deep Learning. J Big Data, 2019, 6, 60.

S. Pudumalar, S. Muthuramalingam, Hydra: An ensemble deep learning recognition model for plant diseases. Journal of Engineering Research, 2023,

Picon, M. Seitz, A. Alvarez-Gila, P. Mohnke, A. Ortiz-Barredo, and J. Echazarra, Crop conditional convolutional neural networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Computers and Electronics in Agriculture, 2019, 167, 105093.

Q. Wang, F. Qi, M. Sun, J. Qu, and J. Xue. Identification of tomato disease types and detection of infected areas based on deep convolutional neural networks and object detection techniques. Computational Intelligence and Neuroscience, 2019, 15 pages.

J. G. A. Barbedo, Factors influencing the use of deep learning for plant disease recognition. Biosystems Engineering, 2018, 172, 84–91.

T. H. Meen, Institute of Electrical and Electronics Engineers, and National Formosa University, and International Institute of Knowledge Innovation and Invention, IoT, communication, and engineering. In 2019 IEEE Eurasia Conference on IoT, Communication, and Engineering (IEEE ECICE 2019), 3-6, Yunlin, Taiwan, October 2019.

J. Bell, H. M. Dee. Leaf segmentation through the classification of edges, 2019, Retrieved from

M. H. Saleem, J. Potgieter, & K. M. Arif, Plant disease detection and classification by deep learning. Plants, 2019, 8(11), 32–34.

T. R. Gadekallu, D. S. Rajput, M. P. K. Reddy, A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. Journal of Real-Time Image Processing, 2021, 18(4), 1383–1396.

E. K. Nithish, M. Kaushik, P. Prakash, R. Ajay, S. Veni, Tomato leaf disease detection using a convolutional neural network with data augmentation. In Proceedings of the 5th International Conference on Communication and Electronics Systems, 2020, ICCES, 1125–1132, Coimbatore, India.

S. Verma, A. Chug, A. P. Singh, Application of convolutional neural networks for evaluation of disease severity in tomato plant. Journal of Discrete Mathematical Sciences and Cryptography, 2020, 23(1), 273–282.

P. Wspanialy, M. Moussa, A detection and severity estimation system for generic diseases of tomato greenhouse plants. Computers and Electronics in Agriculture, 2020, 178, 105701.

R. Karthik, M. Hariharan, S. Anand, P. Mathikshara, A. Johnson, & R. Menaka, Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing Journal, 2020, 86, 105933.

Ahmad, M. Hamid, S. Yousaf, S. T. Shah, M. O. Ahmad, Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity, 2020, 6 pages.

S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. Gino Sophia, B. Pavithra, Tomato leaf disease detection using deep learning techniques. In Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020, 979–983.

S. H. Lee, H. Goëau, P. Bonnet, A. Joly, New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture, 2020, 170, 105220.

Ü. Atila, M. Uçar, K. Akyol, E. Uçar, Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 2021, 61, 101182.

M. Agarwal, A. Singh, S. Arjaria, A. Sinha, S. Gupta, ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science, 2019, 167, 293–301.

L. C. Ngugi, M. Abelwahab, M. Abo-Zahhad, Tomato leaf segmentation algorithms for mobile phone applications using deep learning. Computers and Electronics in Agriculture, 2020, 178, 105788.

J. Chen, J. Chen, D. Zhang, Y. A. Nanehkaran, Y. Sun, A cognitive vision method for the detection of plant disease images. Machine Vision and Applications, 2021, 32(1), 1–18.

V. G. Krishnan, J. Deepa, P. V. Rao, V. Divya, S. Kaviarasan, An automated segmentation and classification model for banana leaf disease detection. Journal of Applied Biology & Biotechnology, 2022, 10(1), 213–220.

Fuentes, S. Yoon, S. C. Kim, D. S. Park, A robust deep learning-based detector for real-time tomato plant diseases and pests recognition. Sensors (Switzerland), 2017, 17(9).

R. K. Singh, A. Tiwari, & R. K. Gupta, Deep transfer modeling for classification of maize plant leaf disease. Multimedia Tools and Applications, 2022, 81(5), 6051–6067.

Elaraby, W. Hamdy, & M. Alruwaili, Optimization of deep learning model for plant disease detection using particle swarm optimizer. Computers, Materials and Continua, 2022, 71(2), 4019–4031.

Wan-jie Liang, Hong Zhang, Gu-feng Zhang, & Hong-xin Cao, Rice Blast Disease Recognition Using a Deep Convolutional Neural Network. Scientific Reports - a Nature Research Journal, Article no. 2869, 2019, doi: 10.1038/s41598-019-38966-0.

Yang Lu, Shujuan Yi, Nianyin Zeng, Yurong Liu, & Yong Zhang, Identification of Rice Diseases using Deep Convolutional Neural Networks. Neuro-Computing, 2017, 267, 378-384, Elsevier.

M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, D. Stefanovic, Solving current limitations of deep learning based approaches for plant disease detection, Symmetry, 2019, 11, 939.

J. Cui, X. Zhang, W. Wang, L. Wang, Integration of optical and SAR remote sensing images for crop type mapping based on a novel object-oriented feature selection method, Int. J. Agric. Biol. Eng, 2020, 13, 178–190.

V. Ananthi, Fused segmentation algorithm for the detection of nutrient deficiency in crops using SAR images, in: Artificial Intelligence Techniques for Satellite Image Analysis, Springer, 2020, pp. 137–159.

T. Baidar, Rice Crop Classification and Yield Estimation Using Multi-Temporal Sentinel-2 Data: a Case Study of Terrain Districts of Nepal, 2020.

K. Lagos-Ortiz, J. Medina-Moreira, A. Alarcon-Salvatierra, ´ M.F. Moran, ´ J. del Cioppo-Morstadt, R. Valencia-García, Decision support system for the control and monitoring of crops, in: 2nd International Conference on ICTs in, Agronomy and Environment, 2019, pp. 20–28.

S. Das, S. Sengupta, Feature extraction and disease prediction from paddy crops using data mining techniques, in: Computational Intelligence in Pattern Recognition, Springer, 2020, pp. 155–163.

T.K. Fegade, B. Pawar, Crop prediction using artificial neural network and support vector machine, in: Data Management, Analytics and Innovation, Springer, 2020, pp. 311–324.

R.G. Hammer, P.C. Sentelhas, J.C.J.S.T. Mariano, Sugarcane Yield Prediction through Data Mining and Crop Simulation Models, vol. 22, 2020, pp. 216–225.

R. Chaudhari, S. Chaudhari, A. Shaikh, R. Chiloba, T. Khadtare, Soil fertility prediction using data mining techniques, International Journal of Future Generation Communication and Networking, 2020, 9 (Issue 6).

M. Champaneri, D. Chachpara, C. Chandvidkar, M. Rathod, Crop yield prediction using machine learning, International Journal of Scientific Research, 2020, Volume 9.

K. Feng, R.S. Tian, Forecasting Reference Evapotranspiration Using Data Mining and Limited Climatic Data 54, Taylor Francis, 2020, pp. 363–371.

S.H. Bhojani, N.J.N.C. Bhatt, Applications, Wheat Crop Yield Prediction Using New Activation Functions in Neural Network, 2020, pp. 1–11.

C. K. M and M. Basthikodi, "Machine Learning Approaches for Abandoned Luggage Detection," 2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India, 2023, pp. 8-12, doi: 10.1109/DISCOVER58830. 2023.10316663.

H. Garg, Neutrality operations-based Pythagorean fuzzy aggregation operators and its applications to multiple attribute group decision-making processJournal of Ambient Intelligence and Humanized Computing, 2019, 1–21.

S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S.G. Sophia, B. Pavithra, Tomato leaf disease detection using deep learning techniques, in: 2020 5th International Conference on Communication and lectronics Systems, (ICCES), 2020, pp. 979–983.

G. Wang, Y. Sun, J. Wang, Automatic image-based plant disease severity estimation using deep learning, Computational Intelligence and Neuroscience, 2017, 1–8. 0.1155/2017/2917536, 2917536

Mustafa Basthikodi, Ananth Prabhu, & Anush Bekal. (2021). Performance Analysis of Network Attack Detection Framework using Machine Learning. Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC), 1(1),11–22.

S. H., “Performance Evolution of Face and Speech Recognition System Using DTCWT and MFCC Features”. Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 3, Apr. 2021, pp. 3395-04,

M. Basthikodi and W. Ahmed, "Classifying a program code for parallel computing against HPCC," 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India, 2016, pp. 512-516, doi: 10.1109/PDGC.2016.7913248.




How to Cite

Prameetha Pai. (2024). Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2064–2077. Retrieved from



Research Article