Abstract
Plant diseases lead to a reduction in both quality and quantity of agricultural production. 50% of agricultural losses are due to these diseases. Due to poverty and lack of infrastructures in some countries, their identification remains difficult. Plant pathologists use several techniques to identify these diseases. But these techniques are time-consuming and relatively expensive for farmers. Nowadays, several models based on image processing (IP) techniques, machine learning (ML) algorithms and deep learning (DL) algorithms have been proposed for automatic detection and identification of plant diseases. In this study, we divided these models into two groups: models based on IP and classical ML algorithms, and those based on DL. DL coupled with the transfer learning (TL) technique has become the most widely used method because of its impressive performance. The critical analysis of these models has allowed us to identify potential challenges in the field of automatic plant disease diagnosis.
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The portion of the population that has access to the Internet.
References
Ait Elkadi, K., Bakouri, S., Belbrik, M., Hajji, H., Chtaina, N.: Experimentation of model for early detection of tomato diseases by deep learning. Rev. Marocain. Protect. Plant. 14, 19–30 (2020)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016, 1–11 (2016)
Fuentes, A., Yoon, S., Kim, S., Park, D.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017)
Arivazhagan, S., Vineth Ligi, S.: Mango leaf diseases identification using convolutional neural network. Int. J. Pure Appl. Math. 120(6), 11067–11079 (2018)
Harvey, C.A., et al.: Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. Philos. Trans. Roy. Soc. Lond. B: Biol. Sci. 369, 1639 (2014)
Mohanty, S.P., Hughes, D., Salathé, M.: Using deep learning for image-based plant disease detection. Comput. Electron. Agricult. 173, 105393 (2020)
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.P.: Deep learning for image-based cassava disease detection. Front. Plant Sci. (2017). https://doi.org/10.3389/fpls.2017.01852
Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., AlRahamneh, Z.: Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17(1), 31–38 (2011). https://doi.org/10.5120/2183-2754
Li, Y., Nie, J., Chao, X.: Do we really need deep CNN for plant diseases identification? Comput. Electron. Agric. 178, 105803 (2020)
Bashish, D.A., Braik, M., Bani-Ahmad, S.: Detection and classification of leaf diseases using K-means-based segmentation and neural-networks-based classification. Inf. Technol. J. 10, 267–275 (2011)
Raj, M., Atiquzzaman, M., Gupta, S., Chamola, V., Elhence, A., Garg, T., Niyato, D.: A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0 (2021)
MahmudulHassan, S.K., Maji, Arnab Kumar, Jasinski, Michał, Leonowicz, Zbigniew, Jasinska, Elzbieta: Identification of plant-leaf diseases using cnn and transfer-learning approach. Electronics 10(12), 1388 (2021)
Mokhtar, U., Ali, M.A.S., Hassanien, A.E., Hefny, H.: Identifying two of tomatoes leaf viruses using support vector machine. In: Mandal, J.K., Satapathy, S.C., Sanyal, M.K., Sarkar, P.P., Mukhopadhyay, A. (eds.) Information Systems Design and Intelligent Applications. AISC, vol. 339, pp. 771–782. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2250-7_77
Geetharamani, G., Arun, P.J.: Identification of plant leaf diseases using a nine- layer deep convolutional neural network. Comput. Electr. Eng. 76, 323–338 (2019)
H. Cartwright, Ed (2015). Artificial Neural Networks, Humana Press.
Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)
Gensheng, H.G., Wang, H., Zhang, Y., Wan, M.: Detection and severity analysis of tea leaf blight based on deep learning. Comput. Electric. Eng. 90, 107023 (2021)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 1–11 (2016)
Chouhan, S.S., Kaul, A., Singh, U.P., Jain, S.: Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. IEEE Access 6, 8852–8863 (2018)
Steinwart, I., Christmann, A.: Support Vector Machines. Springer Science & Business Media, New York (2008)
Tian, J., Hu, Q., Ma, X.X., Han, M.: An improved kpca/ga-svm classication model for plant leaf disease recognition. J. Comput. Inf. Syst. 8(18), 7737–7745 (2012)
Kaur, S., Pandey, S., Goel, S.: Semi- automatic leaf disease detection and classification system for soybean cultivation. IET Image Proc. 12(6), 1038–1048 (2018)
Camargo, A., Smith, J.S.: An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosys. Eng. 102(1), 9–21 (2009)
Sutrodhor, N., Molla Rashied, Md., Firoz, P.K., Nur, T.: Mango leaf ailment detection using neural network ensemble and support vector machine. Int. J. Comput. Appl. 181(13), 31–36 (2018)
Kuo, Y.H.: Detection and classification of areca nuts with machine vision. Comput. Math. Appl. 64, 739–746 (2012)
Saleem, M.H., Potgieter, J., Arif, K.M. Plant Disease Detection and Classification by Deep Learning (2019)
Khilar, T.S.R., Subaja Christo, M.: A comparative analysis on plant pathology classification using deep learning architecture – Resnet and VGG19. Mater. Today: Proc. (2021)
Ramcharan, A., et al. A mobile-based deep learning model for cassava disease diagnosis. Front. Plant Sci. 10 (2019)
Chen, J., Chen, J., Zhang, D., Sun, Y., Nanehkaran, Y.A.: Using deep transfer learning for image-based plant disease identification. Comput. Electron. Agric. 173, 105393 (2020)
Oyewola, D.O., Dada, E.G., Misra, S., Damaševicius, R.: Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing. PeerJ Comput. Sci. 7, e352 (2021)
Arnal Barbedo, J.G.: Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus 2(1), 1–12 (2013)
Wang, G., Sun, Y., Wang, J.: Automatic image-based plant disease severity estimation using deep learning. Comput. Intell. Neurosci. 2017, 2917536 (2017)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas), pp. 2818–2826 (2016)
Pham, T.N., Tran, L.V., Dao, S.V.T.: Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access 8, 189960–189973 (2020)
Singh, U.P., Chouhan, S.S., Jain, S., Jain, S.: Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7, 43721–43729 (2019)
Gulavnai, S., Patil, R.: Deep learning for image based mango leaf disease detection. Int. J. Recent Technol. Eng. 8(3S3), 54–56 (2019)
Singh, P., Verma, A., Alex, J.S.R.: Disease and pest infection detection in coconut tree through deep learning techniques. Comput. Electron. Agric. 182, 105986 (2021)
Saleem, R., Shah, J.H., Sharif, M., Ansari, G.J.: Mango leaf disease identification using fully resolution convolutional network. Comput. Mater. Continua 69(3), 3581–3601 (2021)
Ozguven, M.M., Adem, K.: Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Phys. A Stat. Mech. Appl. 535, 122537 (2019)
Lumini, A., Nanni, L.: Deep learning and transfer learning features for plankton classification. Ecol. Inf. (2019)
Kumar, P., Kumar, R., Gupta, M.: Deep learning based analysis of ophthalmology: a systematic review. EAI Endors. Trans. Pervas. Health Technol. 7(29), e4 (2021)
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Faye, D., Diop, I. (2022). Survey on Crop Disease Detection and Identification Based on Deep Learning. In: Mambo, A.D., Gueye, A., Bassioni, G. (eds) Innovations and Interdisciplinary Solutions for Underserved Areas. InterSol 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-23116-2_18
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