The article explores the use of convolutional neural networks (CNNs) in the diagnosis and identification of plant diseases and pests. Various methods of plant disease diagnosis, features of datasets, and challenges in this research direction are considered. The article discusses a five-step methodology for determining plant diseases, including data collection, preprocessing, segmentation, feature extraction, and classification. Different deep learning architectures enabling fast and efficient plant disease diagnosis are investigated. Innovative trends and issues in this field requiring further research and attention from the scientific community are highlighted
[1] F. Fina, P. Birch, R. Young, J. Obu, B. Faithpraise, and C. Chatwin, "Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters," International Journal of Advanced Biotechnology and Research, vol. 4, pp. 189-199, 2013.
[2] M. H. Saleem, S. Khanchi, P. Potgieter, and K. M. Arif, "Image-based plant disease identification by deep learning meta-architectures," Plants, vol. 9, no. 11, pp. 1451, 2020. https://doi.org/10.3390/plants9111451
[3] J. Amara, B. Bouaziz, and A. Algergawy, "A Deep Learning-based Approach for Banana Leaf Diseases Classification," 2017. Available at: https://dl.gi.de/handle/20.500.12116/944
[4] K. P. Panigrahi, H. Das, A. K. Sahoo, and S. C. Moharana, "Maize leaf disease detection and classification using machine learning algorithms," in Advances in Intelligent Systems and Computing, Springer, Germany, 2020, pp. 659-669. https://doi.org/10.1007/978-981-15-2414-1_66
[5] B. B. Benuwa, Y. Zhan, B. Ghansah, D. K. Wornyo, and F. B. Kataka, "A review of deep machine learning," International Journal of Engineering Research in Africa, vol. 24, pp. 124-136, 2016. https://doi.org/10.4028/www.scientific.net/JERA.24.124
[6] S. B. Kotsiantis, D. Kanellopoulos, and P. E. Pintelas, "Data preprocessing for supervised learning," International Journal of Computer Science, vol. 1, pp. 111-117, 2006.
[7] U. Shruthi, V. Nagaveni, and B. K. Raghavendra, "A Review on Machine Learning Classification Techniques for Plant Disease Detection," in 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, Institute of Electrical and Electronics Engineers Inc., United States, 2019, pp. 281-284. https://doi.org/10.1109/ICACCS.2019.8728415
[8] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, "Deep neural networks based recognition of plant diseases by leaf image classification," Computational Intelligence and Neuroscience, vol. 2016, p. 3289801, 2016. https://doi.org/10.1155/2016/3289801
[9] Nagasubramanian, K., Jones, S., Singh, A.K. et al. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15, 98 (2019). https://doi.org/10.1186/s13007-019-0479-8
[10] Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, "Identification of rice diseases using deep convolutional neural networks," Neurocomputing, vol. 267, pp. 378-384, 2017. https://doi.org/10.1016/j.neucom.2017.06.023
[11] B. A. M. Ashqar, B. S. Abu-Nasser, and S. S. Abu-Naser, "Plant Seedlings Classification Using Deep Learning," 2019.
[12] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkarana, "Using deep transfer learning for image-based plant disease identification," Computers and Electronics in Agriculture, vol. 173, p. 105393, 2020. https://doi.org/10.1016/j.compag.2020.105393
[13] J. G. Arnal Barbedo, "Plant disease identification from individual lesions and spots using deep learning," Biosystems Engineering, vol. 180, pp. 96-107, 2019. https://doi.org/10.1016/j.biosystemseng.2019.02.002
[14] J. G. A. Barbedo, "Factors influencing the use of deep learning for plant disease recognition," Biosystems Engineering, vol. 172, pp. 84-91, 2018. https://doi.org/10.1016/j.biosystemseng.2018.05.013
[15] S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using deep learning for image-based plant disease detection," Frontiers in Plant Science, vol. 7, p. 1419, 2016. https://doi.org/10.3389/fpls.2016.01419
[16] S. Dara and P. Tumma, "Feature Extraction by Using Deep Learning: A Survey," in Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology, ICECA 2018, Institute of Electrical and Electronics Engineers Inc., United States, 2018, pp. 1795-1801.
[17] K. Karthikayani and A. R. Arunachalam, "A survey on deep learning feature extraction techniques," in AIP Conference Proceedings, American Institute of Physics Inc., College Park, Maryland, 2020. https://doi.org/10.1063/5.0028564
[18] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artificial Intelligence Review, vol. 53, no. 8, pp. 5455-5516, 2020. https://doi.org/10.1007/s10462-020-09825-6
[19] A. Zbakh, Z. A. Mdaghri, A. Benyoussef, A. El Kenz, M. El Yadari, et al., "Spectral classification of a set of hyperspectral images using the convolutional neural network, in a single training," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 10, no. 6, 2019. https://hal.science/hal-02172017 https://doi.org/10.14569/IJACSA.2019.0100634
[20] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, "Backpropagation applied to handwritten zip code recognition," Neural Computation, vol. 1, no. 4, pp. 541-551, 1989. https://doi.org/10.1162/neco.1989.1.4.541
[21] H. Durmus, E. O. Gunes, and M. Kirci, "Disease Detection on the Leaves of the Tomato Plants by Using Deep Learning," in 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017, Institute of Electrical and Electronics Engineers Inc., United States, 2017. https://doi.org/10.1109/Agro-Geoinformatics.2017.8047016
[22] Z. Ibrahim, N. Sabri, and D. Isa, "Multi-maxpooling Convolutional Neural Network for Medicinal Herb Leaf Recognition," in Proceedings of the 6th IIAE International Conference on Intelligent Systems and Image Processing 2018, The Institute of Industrial Applications Engineers, Japan, 2018. https://doi.org/10.12792/icisip2018.060
[23] P. Ramachandran, B. Zoph, and Q. V. Le, "Searching for Activation Functions," in 6th International Conference on Learning Representations, ICLR 2018-Workshop Track Proceedings, 2017. http://arxiv.org/abs/1710.05941
[24] N. Fatihah Sahidan, A. K. Juha, N. Mohammad, and Z. Ibrahim, "Flower and leaf recognition for plant identification using convolutional neural network," Indonesian Journal of Electrical Engineering and Computer Science, vol. 16, no. 2, pp. 737-743, 2019. https://doi.org/10.11591/ijeecs.v16.i2.pp737-743
[25] M. M. Saufi, M. A. Zamanhuri, N. Mohammad, and Z. Ibrahim, "Deep learning for roman handwritten character recognition," Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 2, pp. 455-460, 2018. https://doi.org/10.11591/ijeecs.v12.i2.pp455-460
[26] D. Jiang, G. Li, Y. Sun, J. Hu, J. Yun, and Y. Liu, "Manipulator grabbing position detection with information fusion of color image and depth image using deep learning," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 12, pp. 10809-10822, 2021. https://doi.org/10.1007/s12652-020-02843-w
[27] B. Liu, Y. Zhang, D. He, and Y. Li, "Identification of apple leaf diseases based on deep convolutional neural networks," Symmetry, vol. 10, p. 11, 2017. https://doi.org/10.3390/sym10010011
[28] S. M. Omer, K. Z. Ghafoor, and S. K. Askar, "An intelligent system for cucumber leaf disease diagnosis based on the tuned convolutional neural network algorithm," Mobile Information Systems, vol. 2022, p. 8909121, 2022. https://doi.org/10.1155/2022/8909121
[29] S. Hernández and J. L. López, "Uncertainty quantification for plant disease detection using Bayesian deep learning," Applied Soft Computing Journal, vol. 96, p. 106597, 2020. https://doi.org/10.1016/j.asoc.2020.106597
[30] K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018. https://doi.org/10.1016/j.compag.2018.01.009
[31] A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, "Deep learning for image-based cassava disease detection," Frontiers in Plant Science, vol. 8, p. 1852, 2017. https://doi.org/10.3389/fpls.2017.01852
[32] A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, "A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition," Sensors (Switzerland), vol. 17, no. 9, p. 2022, 2017. https://doi.org/10.3390/s17092022
[33] F. Hutter, J. Lücke, and L. Schmidt-Thieme, "Beyond manual tuning of hyperparameters," KI-Kunstliche Intelligenz, vol. 29, no. 4, pp. 329-337, 2015. https://doi.org/10.1007/s13218-015-0381-0
[34] A. H. Victoria and G. Maragatham, "Automatic tuning of hyperparameters using Bayesian optimization," Evolving Systems, vol. 12, no. 1, pp. 217-223, 2021. https://doi.org/10.1007/s12530-020-09345-2
[35] P. Angelov and A. Sperduti, "Challenges in Deep Learning," 2016. Available from: https://eprints.lancs.ac.uk/id/eprint/134273
[36] L. Rice, E. Wong, and J. Z. Kolter, "Overfitting in Adversarially Robust Deep Learning," in Proceedings of Machine Learning Research, 2020.
[37] M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, and D. Stefanovic, "Solving current limitations of deep learning based approaches for plant disease detection," Symmetry, vol. 11, no. 7, p. 939, 2019.
[38] J. Brownlee, "Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions," 2018. https://doi.org/10.3390/sym11070939