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Chithra Devi, M.
- A Comprehensive Review on Diagnosis and Classification of paddy Leaf Diseases Using Advanced Computer Vision Technologies
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Authors
Affiliations
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, IN
2 Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, IN
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, IN
2 Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 2973-2986Abstract
Food is required for human survival. Paddy is a vital food crop serving 60% of the Indian population. Food quality is determined by the plant yield. Unfavorable environmental circumstances, soil fertility, bacteria, viruses, nematodes, fertilizer use, and the absence of nutritional shortages substantially influence plant yield. As a result, it is critical to protect the plants from illness. Crop yield must be improved to meet food scarcity of growing population. Although disease symptoms are apparent in various parts of plant like leaves, stem, fruits and stem, the infections are commonly observed in the leaves. Understanding plant pathology plays a vital role in disease detection. Early detection of diseases is a prompt intervention that aids the farmers in controlling disease spread, resulting in increased agricultural quantity and quality. Image processing techniques with advanced computer vision technologies like machine learning and deep learning have proven the automation of plant disease diagnosis precisely. The main objectives of this research are to investigate computer vision technologies for the early identification of plant diseases and help novice researchers in the same domain learn about plant diseases and the methodologies for disease detection in paddy plant leaves. Consequently this manuscript reviews significant paddy plant infections, highlights related study of tools and techniques, current research, limitations and conclusions for future research in this field.Keywords
Paddy Disease Detection, Machine Learning, Deep Learning, Preprocessing, Segmentation, Feature Extraction, Classification, Convolutional Neural Network.
References
- T. Gayathri Devi and P. Neelamegam, “Image Processing based Rice Plant Leaves Diseases in Thanjavur, Tamilnadu”, Cluster Computing, Vol. 22, No. 6, pp. 13415-13428, 2019.
- S. Kaur, S. Pandey and S. Goel, “Plants Disease Identification and Classification Through Leaf Images: A Survey”, Archives of Computational Methods in Engineering, Vol. 26, No. 2, pp. 507-530, 2019.
- X.E. Pantazi, D. Moshou and A.A. Tamouridou, “Automated Leaf Disease Detection in Different Crop Species through Image Features Analysis and One Class Classifiers”, Computers and Electronics in Agriculture, Vol. 156, pp. 96-104, 2019.
- C.H. Bock, G.H. Poole, P.E. Parker and T.R. Gottwald, “Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging”, Critical Reviews in Plant Sciences, Vol. 29, No. 2, pp. 59-107, 2010.
- S. Arivazhagan, R.N. Shebiah, S. Ananthi and S. Vishnu Varthini, “Detection of Unhealthy Region of Plant Leaves and Classification of Plant Leaf Diseases using Texture Features”, Agricultural Engineering International: CIGR Journal, Vol. 15, No. 1, pp. 211-217, 2013.
- P.K. Sethy, N.K. Barpanda, A.K. Rath and S.K. Behera, “Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey”, Procedia Computer Science, Vol. 167, pp. 516-530, 2020.
- P.K. Sethy, N.K. Barpanda, A.K. Rath, S.K. Behera, “Deep Feature Based Rice Leaf Disease Identification using Support Vector Machine”, Computers and Electronics in Agriculture, Vol. 175, pp. 1-13, 2020.
- H.B. Prajapati and V.K. Dabhi, “Detection and Classification of Rice Plant Diseases”, Intelligent Decision Technologies, Vol. 11, No. 3, pp. 357-373, 2017.
- G.B. Lucas, C.L. Campbell and L.T. Lucas, “Introduction to Plant Diseases: Identification and Management”, Springer Science and Business Media, 1992.
- J. Qin, C. Wang, L. Wang, S. Zhao and J. Wu, “Defense and Counter-Defense in Rice-Virus Interactions”, Phytopathology Research, Vol. 1, pp. 1-6, 2019.
- S. Uguz and N. Uysal, “Classification of Olive Leaf Diseases using Deep Convolutional Neural Networks”, Neural Computing and Applications, Vol. 33, No. 9, pp. 4133-4149, 2021.
- S. Ramesh and D. Vydeki, “Rice Blast Disease Detection and Classification using Machine Learning Algorithm”, Proceedings of International Conference on Micro-Electronics and Telecommunication Engineering, pp. 255-259, 2018.
- N.N. Kurniawati, S.N.H.S Abdullah, S. Abdullah and S. Abdullah, “Texture Analysis for Diagnosing Paddy Disease”, Proceedings of International Conference on Electrical Engineering and Informatics, pp. 23-27, 2009.
- G. Zhang, T. Xu, Y. Tian, H. Xu, J. Song and Y. Lan, “Assessment of Rice Leaf Blast Severity using Hyper Spectral Imaging during Late Vegetative Growth”, Australasian Plant Pathology, Vol. 49, No. 5, pp. 571-578, 2020.
- A. Adeel, M.A. Khan, M. Sharif, F. Azam, T. Umer and S. Wan, “Diagnosis and Recognition of Grape Leaf Diseases: An automated system based on a Novel Saliency approach and Canonical Correlation Analysis based multiple features fusion”, Sustainable Computing: Informatics and Systems, Vol. 24, pp. 1-12, 2019.
- C. Usha Kumari, S Jeevan Prasad and G. Mounika, “Leaf Disease Detection: Feature Extraction with K-Means Clustering and Classification with ANN”, Proceedings of International Conference on Computing Methodologies and Communication, pp. 1095-1098, 2019.
- A. Nigam, A.K. Tiwari and A. Pandey, “Paddy Leaf Diseases Recognition and Classification using PCA and BFO-DNN Algorithm by Image Processing”, Materials Today: Proceedings, Vol. 33, pp. 4856-4862, 2020.
- A. Rao and S.B. Kulkarni, “A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods”, The International Journal of Electrical Engineering and Education, Vol. 23, pp. 1-9, 2020.
- A.A.N. Ahmed, H.M.F. Haque, A. Rahman, M.S. Ashraf and S. Shatabda, “Wavelet and Pyramid Histogram Features for Image-Based Leaf Detection”, Proceedings of International Conference on Emerging Technologies in Data Mining and Information Security, pp. 269-278, 2019.
- T. Gayathri Devi and P. Neelamegam, “Paddy Leaf Disease Detection using SVM with RBFN Classifier”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 15, pp. 699-710, 2017.
- T. Islam, M. Sah, S. Baral and R.R. Choudhury, “A Faster Technique on Rice Disease Detection using Image Processing of Affected Area in Agro-Field”, Proceedings of International Conference on Inventive Communication and Computational Technologies, pp. 62-66, 2018.
- D. AI. Bashish, M. Braik and S. Bani-Ahmad, “Detection and Classification of Leaf Diseases using K-Means-Based Segmentation and Neural-Networks-Based Classification”, Information Technology Journal, Vol. 10, No. 2, pp. 267-275, 2011.
- V. Singh and A.K Misra, “Detection of Plant Leaf Diseases using Image Segmentation and Soft Computing Techniques”, Information Processing in Agriculture, Vol. 4, No. 1, pp. 41-49, 2017.
- S. Ramesh and D. Vydeki, “Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm”, Information Processing in Agriculture, Vol. 7, No. 2, pp. 249-260, 2020.
- S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S.G. Sophia and B. Pavithra, “Tomato Leaf Disease Detection using Deep Learning Techniques”, Proceedings of International Conference on Communication and Electronics Systems, pp. 979-983, 2020.
- F.T. Pinki, N. Khatun and S.M.M. Islam, “Content based Paddy Leaf Disease Recognition and Remedy Prediction using Support Vector Machine”, Proceedings of International Conference on Computer and Information Technology, pp. 1-5, 2017.
- D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat and N. Batra, “PlantDoc: A Dataset for Visual Plant Disease Detection”, Proceedings of International Conference on Data Science, pp. 249-253, 2020.
- G. Dhingra, V. Kumar and H.D. Joshi, “Quality Assessment of Leaves Quality using Texture and DWT based Local Feature Extraction Analysis”, Chemometrics and Intelligent Laboratory Systems, Vol. 208, pp. 1-15, 2021.
- R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Prentice Hall, 2008.
- S. Pavithra, A. Priyadharshini, V. Praveena and T. Monika, “Paddy Leaf Disease Detection using SVM Classifier”, International Journal of Communication and Computer Technologies, Vol. 3, No. 1, pp. 16-20, 2015.
- A.D. Nidhis, C.N.V. Pardhu, K.C. Reddy and K. Deepa, “Cluster Based Paddy Leaf Disease Detection, Classification and Diagnosis in Crop Health Monitoring Unit”, Proceedings of International Conference on Computer Aided Intervention and Diagnostics in Clinical and Medical Images, pp. 281-291, 2019.
- R. Islam and M.R. Islam, “An Image Processing Technique to Calculate Percentage of Disease Affected Pixels of Paddy Leaf”, International Journal of Computer Applications, Vol. 123, pp. 1-12, 2015.
- A.K. Jain, “Fundamentals of Digital Image Processing”, Prentice-Hall, 1989.
- V. Singh, S. Gupta and S. Saini, “A Methodological Survey of Image Segmentation using Soft Computing Techniques”, Proceedings of International Conference on Advances in Computer Engineering and Applications, pp. 419-422, 2015.
- W.K. Mutlag, S.K. Ali, Z.M. Aydam and B.H. Taher, “Feature Extraction Methods: A Review”, Journal of Physics: Conference Series, Vol. 1591, No. 1, pp. 1-14, 2020.
- A. Ramola, A.K. Shakya and D.V. Pham, “Study of Statistical Methods for Texture Analysis and their Modern Evolutions”, Engineering Reports, Vol. 2, No. 4, pp. 1-9, 2020.
- M.A. Saleem, M. Aamir, R. Ibrahim, N. Senan and T. Alyas, “An Optimized Convolution Neural Network Architecture for Paddy Disease Classification”, Computers, Materials and Continua, Vol. 71, No. 3, pp. 6053-6067, 2022.
- T. Vadivel and R. Suguna, “Automatic Recognition of Tomato Leaf Disease using Fast Enhanced Learning with Image Processing, Acta Agriculturae Scandinavica, Section B - Soil and Plant Science, Vol. 72, No. 1, pp. 312-324, 2022.
- S.H. Lee, H. Goeau, P. Bonnet and A. Joly, “New Perspectives on Plant Disease Characterization based on Deep Learning”, Computers and Electronics in Agriculture, Vol. 170, pp. 1-23, 2020.
- P. Bedi and P. Gole, “Plant Disease Detection using Hybrid Model based on Convolutional Autoencoder and Convolutional Neural Network”, Artificial Intelligence in Agriculture, Vol. 5, pp. 90-101, 2021.
- R.Sujatha, J.M. Chatterjee, N.Z. Jhanjhi and S.N. Brohi, “Performance of Deep Learning vs Machine Learning in Plant Leaf Disease Detection”, Microprocessors and Microsystems, Vol. 80, pp. 103615-103627, 2021.
- S. Vallabhajosyula, V. Sistla and V.K.K. Kolli, “Transfer Learning-Based Deep Ensemble Neural Network for Plant Leaf Disease Detection”, Journal of Plant Diseases and Protection, Vol. 129, No. 3, pp. 545-558, 2022.
- S.J. Pethibridge and S.C. Nelson, “Leaf Doctor: A New Portable Application for Quantifying Plant Disease Severity”, Plant Disease, Vol. 99, No. 10, pp. 1310-1316, 2015.