Classification Models for Plant Diseases Diagnosis: A Review
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Abstract
Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved.
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Dhingra, G., Kumar, V., & Joshi, H. D. (2018). Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77(15), 19951–20000.
Harvey, C. A., Rakotobe, Z. L., Rao, N. S., Dave, R., Razafimahatratra, H., Rabarijohn, R. H., et al. (2014). Extreme vulnerability of smallholder farmers to agricultural risks and climate change in madagascar. Philosophical Transactions of The Royal Society B Biological Science 369:20130089. doi: 10.1098/rstb.2013.008
Sanchez, P. A., and Swaminathan, M. S. (2005). Cutting world hunger in half. Science 307, 357–359. doi: 10.1126/science.1109057
Chen, J., Chen, J., Zhang, D., Sun, Y., Nanehkaran, Y.A. (2020) Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture , 173, 105393.
Bai, X., Cao, Z., Zhao, L., Zhang, J., Lv, C., Li, C., Xie, J. (2018) Rice heading stage automatic observation by multi-classifier cascade-based rice spike detection method. Agricultural and Forest Meteorology. 259, pp. 260– 270, doi: 10.1016/j.agrformet.2018.05.001.
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes,D.P. (2017). Deep Learning for Image-Based Cassava Disease Detection. Frontiers in Plant Science, 8, 1852, doi:10.3389/fpls.2017.01852.
Upadhyay, S.K., Kumar, A. (2022). A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology 14, pp.185–199 (2022). https://doi.org/10.1007/s41870-021-00817-5
Sabrol, H., Kumar, S. (2015). Recent studies of image and soft computing techniques for plant disease recognition and classifcation. International Journal of Computer Application 126(1): pp. 44–55. https://doi.org/10.5120/ijca2 015905982
Singh, V., Gupta, S., Saini, S. (2015a). A methodological survey of image segmentation using soft computing techniques. In: Conference proceeding— 2015 international conference on advances in computer engineering and applications, ICACEA 2015,pp 419–422. https://doi.org/10.1109/ICACEA.2015.7164741
Singh, V., Varsha, Misra, A.K. (2015b). Detection of unhealthy region of plant leaves using image processing and genetic algorithm. In: Conference proceeding—2015 International conference on advances in computer engineering and applications, ICACEA 2015, pp 1028–1032. https://doi.org/10.1109/ICACE A.2015.7164858
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Arbor, A., 2015. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA, pp. 1– 9.
LeCun, Y., Bengio, Y., Hinton, G., (2015). Deep learning. Nature 521, 436– 444
Cruz AC, Luvisi A, De Bellis L and Ampatzidis Y (2017). X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion. Frontiers in Plant Science 8:1741.doi: 10.3389/fpls.2017.01741
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
Gour, M., Jain, S. (2020). Stacked convolutional neural network for diagnosis of covid-19 disease from x-ray images. arXiv preprint arXiv:2006.13817.
Gour, M., Jain, S., and Kumar, T. S. (2020). Residual learning-based CNN for breast cancer histopathological image classification. International Journal of Imaging Systems and Technology, 30(3), pp.621-635.
Abdel-Hamid, O., Mohamed, A.R., Jiang, H., Deng, L., Penn, G., Yu, D. (2014). Convolutional neural networks for speech recognition. IEEE/ACM Trans. Audio, Speech, Language Process. 22 (10), 1533–1545.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S. et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115: 211–252.
Kurniawati,N.N., Abdullah,S.N.H.S., Abdullah,S., Abdullah,S. (2009). Investigation on Image Processing Techniques for Diagnosing Paddy Diseases. International Conference of Soft Computing and Pattern Recognition, 2009 IEEE, pp. 272-277.
Ying, G., Miao, L., Yuan, Y., and Zelin, H. (2008). A Study on the Method of Image Pre-Processing for Recognition of Crop Diseases. International Conference on Advanced Computer Control, 2008 IEEE, pp. 202-206.
Cui, D., Zhang, Q., Li, M., Hartman, G.L., and Zhao, Y. (2010). Image Processing Methods for Quantitatively Detecting Soybean Rust from Multispectral Images. Published by Elsevier Ltd, Biosystems Engineering 107(2010), pp. 186-193.
Patil, S. B., & Bodhe, S. K. (2011). Leaf disease severity measurement using image processing. International Journal of Engineering and Technology, 3(5), 297-301.
Chaudhary, P., Chaudhari, A.K., Cheeran, A.N., Godara, S. (2012). Color transform-based approach for disease spot detection on plant leaf. International Journal of Computer Science and Telecommunications 2012;3(6).
Naikwadi, S., and Amoda, N. (2013). Advances in image processing for detection of plant diseases, International Journal of Application or Innovation in Engineering & Management, 2 (11).
Dhaygude, S. B., & Kumbhar, N. P. (2013). Agricultural plant leaf disease detection using image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(1), 599-602.
Muthukannan, K., Latha, P. (2015). A PSO model for disease pattern detection on leaf surfaces. Image Analysis and Stereology, 34(3): 209-216. https://doi.org/10.5566/ias.1227
Rishi, N. and Gill, J.S., (2015). An Overview on Detection and Classification of Plant Diseases in Image Processing. International Journal of Scientific Engineering and Research (IJSER), 3(5).
Zhang, S., Wu, X., You, Z. Zhang, L. (2017). Leaf image-based cucumber disease recognition using sparse representation classification. Computers and Electronics in Agriculture, Volume 134, 2017, Pages 135-141. https://doi.org/10.1016/j.compag.2017.01.014.
Singh, V., and Misra, A.K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, Volume 4, Issue 1, 2017, Pages 41-49, https://doi.org/10.1016/j.inpa.2016.10.005.
Sasakoi, Y., Okamoto, T., Imou, K., and Torii, T. (1999). Automatic diagnosis of plant disease-recognition between healthy and diseased leaf. Agricultural Society of Mechanical Engineers, vol. 61, no. 2, pp. 119–126.
Helly, M. E., Rafea, A., and Salwa-El-Gammal. (2003). An integrated image processing system for leaf disease detection and diagnosis. In Proc. IICAI, pp.1182-1195.
Moshou, D., Bravo C., West, J., Wahlen, S., McCartney, A., Ramon, H. (2004). Automatic detection of yellow rust in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 44 (3), pp. 173-188.
Sammany, M., and Medhat, T. (2007). Dimensionality reduction using rough set approach for two neural networks-based applications. in Rough Sets and Intelligent Systems Paradigms, pp. 639–647, Springer, Berlin, Germany.
Meunkaewjinda, A., Kumsawat, P., Attakitmongcol, K. and kaew, S. (2008). Grape leaf disease detection from color imagery using hybrid intelligent system. proceedings of ECTICON 2008.
Li, B., Liu, Z., Huang, J., Zhang, L., Zhou, W., Shi, J. (2009). Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network. Transactions of the Chinese Society of Agricultural. Engineering, 25 (9) (2009), pp. 143-147.
Liu, L., and Zhou, G. (2009). Extraction of the Rice Leaf Disease Image Based on BP Neural Network”,2009 IEEE.
Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., and Alrahamneh, Z. (2011). Fast and accurate detection and classification of plant diseases. Machine learning 14 (2011).
Mrunalini, R. B., and Deshmukh, P.R. (2011). An application of K-means clustering and artificial intelligence in pattern recognition for crop diseases. International Conference on Advancements in Information Technology ;20. 2011 IPCSIT.
Kai, S., Zhikun, L., Hang, S., Chunhong, G. (2011). A Research of Maize Disease Image Recognition of Corn Based on BP Networks. Third International Conference on Measuring Technology and Mechatronics Automation, 2011 IEEE, pp. 246-249.
Kulkarni, A. H, Ashwin Patil, R.K. (2012) Applying image processing technique to detect plant diseases. International Journal of Modern Engineering Research ;2(5):3661–4.
Wang, H., Li, G., Ma, Z., and Li, X. (2012). Image recognition of plant diseases based on backpropagation networks. In 5th International Congress on, Image and Signal Processing (CISP), 2012pp. 894-900. IEEE.
Jaware, T.H., Badgujar, R.D., and Patil, P.G. (2012). Crop disease detection using image segmentation. Proceedings of "Conference on Advances in Communication and Computing (NCACC'12)”, April 21, 2012.
Owomugisha, G., Quinn, J.A., Mwebaze, E., and Lwasa, J. (2014). Automated vision-based diagnosis of Banana Bacterial Wilt disease and Black Sigatoka Disease. Preceding of the 1’st international conference on the use of mobile ICT in Africa.
Khirade, S.D., and Patil, A.B. (2015). February. Plant Disease Detection Using Image Processing. In International Conference on Computing Communication Control and Automation (ICCUBEA), (pp. 768- 771). IEEE.
Rastogi, A., Arora, R., and Sharma, S. (2015). Leaf disease detection and grading using computer vision technology & fuzzy logic. 2nd International Conference on Signal Processing and Integrated Networks (SPIN)2015.
Sannakki, S.S., and Rajpurohit, V.S. (2015). Classification of Pomegranate Diseases Based on Back Propagation Neural Network. International Research Journal of Engineering and Technology (IRJET), Vol2 Issue: 02.
Rothe, P.R., and Kshirsagar, R.V. (2015). Cotton leaf disease identification using pattern recognition techniques. International Conference on Pervasive Computing (ICPC),2015.
Ghaiwat, S.N., Arora, P. (2014). Detection and classification of plant leaf diseases using image processing techniques: a review. International Journal of Recent Advances in Engineering & Technology;2(3):2347–812. ISSN (Online).
Mokhtar, U., Ali, M.A., Hassanien, A.E., Hefny, H. (2015). Identifying two of tomatoes leaf viruses using support vector machine. In Information Systems Design and Intelligent Applications; Springer: Berlin/Heidelberg, Germany, pp. 771–782.
Vishnu, S., Ram, A.R. (2015). Plant disease detection using leaf pattern: A review. International Journal of Innovative Science, Engineering and Technology, 2(6): 774-780.
Prajapati, H.B., Shah, J.P., Dabhi, V.K. (2017) Detection and classification of rice plant diseases. Journal of Intelligent Decision Technology. 11(3):357– 373.
Jayanthi, G., Archana, K.S., Saritha, A. (2019) Analysis of automatic rice disease classification using image processing techniques. International Journal of Engineering and Advanced Technology (IJEAT) 8(3S):2249–8958.
Nandhini, N., Bhavani, R. (2020). Feature extraction for diseased leaf image classification using machine learning. In: 2020 International Conference on Computer Communication and Informatics (ICCCI). IEEE, pp. 1–4.
Bodapati, J.D., Veeranjaneyulu, N. (2019). Feature extraction and classification using deep convolutional neural networks. Journal of Cyber Security and Mobility. 8(2):261–276. https://doi.org/10.13052/jcsm2245- 1439.825
Barbedo, J.G.A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springer plus 2(1):660–672
Liu, B., Zhang, Y., He, D., Li, Y. (2018). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10 (1), 11.
Agarwal, M., Singh, A., Arjaria, S., Sinha, A., Gupta, S. (2020). ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Computer Science , 167, 293–301.
Barbedo, J.G.A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystem Engineering 180, 96–107.
Trivedi, N. K., Gautam, V., Anand, A., Aljahdali, H.M., Villar, S.G., Anand, D., Goyal, N. and Kadry, S. (2021). Early detection and classification of tomato leaf disease using high-performance deep neural network. Sensors 21, no. 23: 7987. https://doi.org/10.3390/s21237987
Wu, Y., Xu, L., Goodman, E.D. (2021). Tomato Leaf Disease Identification and Detection Based on Deep Convolutional Neural Network. Intelligent Automation and Soft Computing 2021, 28, 561–576
Verma, T., and Dubey, S. (2021) Prediction of diseased rice plant using video processing and LSTM-simple recurrent neural network with comparative study. Multimedia Tools and Application. 80:29267–29298. https://doi.org/10.1007/s11042-021-10889-x
Kumbhar, S., Nilawar, A., Patil, S., Mahalakshmi, B., Nipane, M. (2019). Farmer buddy-web based cotton leaf disease detection using CNN. International Journal of Applied Engineering Research 14(11): pp. 2662–2666.
Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y. (2017). Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378–384.
Patidar, S., Pandey, A., Shirish, B.A., Sriram, A. (2020). Rice Plant Disease Detection and Classification Using Deep Residual Learning. In: Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1240.Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_23
Zhang, X., Qiao, Y., Meng, F., Fan, C., and Zhang, M. (2018). Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks," in IEEE Access, vol. 6, pp. 30370-30377, 2018, DOI: 10.1109/ACCESS.2018.2844405.
Ghazi, M.M., Yanikoglu, B. and Aptoula, E. (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228–235.
Krishnamoorthy, N., Prasad, L.V.N., Kumar, C.S.P., Subedi. B., Abraha, H.B., Sathishkumar, V. E. (2021). Rice leaf diseases prediction using deep neural networks with transfer learning. Environmental Research 2021 Jul; 198:111275. doi: 10.1016/j.envres.2021.111275.
Chen, J., Chen, J., Zhang, D., Sun, Y., Nanehkaran, Y.A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, Volume 173,2020,105393, (2020) https://doi.org/10.1016/j.compag.2020.105393
Sangeetha, R., & Rani, M. (2020). Tomato Leaf Disease Prediction Using Transfer Learning. In Proceedings of the International Advanced Computing Conference 2020, Panaji, India, 5–6. Thangaraj, R., Anandamurugan, S. & Kaliappan, V.K. (2021). Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. Journal of Plant Diseases and Protection. 128, 73–86 . https://doi.org/10.1007/s41348-020-00403-0 .
Hasan, M., Tanawala, B., Patel, K.J. (2019). Deep learning precision farming: tomato leaf disease detection by transfer learning. In: Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), 9 March 2019.
Abas, M.A.H., Ismail, N., Yassin, A.I.M., Taib, M.N. (2018). VGG16 for plant image classification with transfer learning and data augmentation. International Journal of Engineering and Technology 7(4.11), 90–94.
Mathulaprangsan, S., Lanthong, K., Jetpipattanapong, D., Sateanpattanakul, S., Patarapuwadol, S. (2020) Rice diseases recognition using effective deep learning models. 386–389. https://doi.org/10. 1109/ECTIDAMTNCON48261. 2020.9090709.
Islam, M., Shuvo, M., Shamsojjaman, M., Hasan, S., Hossain, M., Khatun, T. (2021). An automated convolutional neural network-based approach for paddy leaf disease detection. International Journal of Advanced Computer Science and Applications (IJACSA). https://doi.org/10.14569/IJACSA.2021. 0120134.
Jadhav, S.B., Udupi, V.R., Patil, S.B. (2020). Identification of plant diseases using convolutional neural networks. International Journal of Information Technology. https://doi.org/10.1007/s41870-020-00437-5
Ghosal, S., and Sarkar, K. (2020). Rice Leaf Diseases Classification Using CNN With Transfer Learning. 2020 IEEE Calcutta Conference (CALCON), 2020, pp. 230-236, doi: 10.1109/CALCON49167.2020.9106423.
Deng, R., Tao, M., Xing, H., Yang, X., Liu, C., Liao, K., Qi, L. (2021) Automatic diagnosis of rice diseases using deep learning. Frontiers in Plant Science. 12:701038. https://doi.org/10.3389/fpls.2021.701038 .