Intelligent Plant Disease Diagnosis Using Machine Learning Techniques- A Review
Keywords:
Intelligent, Plant Disease, Diagnosis, Machine Learning, DatasetsAbstract
No matter how we look at it, there are no chances to live without the flora surrounding us. One can be troubled by a broad range of diseases that attack the integrity of his or her health. Basically, all the plant parts are fruits, stems, roots, leaves, and so on. The time and money in terms of having successfully figured out the disease of a plant are much less than that if a diagnosis error has been made. Sustained economic losses caused by plant disease are due to the facilitation of rot production, which involves the reduction of agricultural product yields and quantities. Creating measures that would halt the destruction of crops due to plant diseases is essential since the contributing factor of 70% of agricultural produce to GDP is high. This group of illnesses must be watched closely since the diseases start as soon as the plants have begun their growing process.
The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.
References
D.S. Wankhede, A. Gamot, K. Motwani, S. Kayande, V. Agrawal, and C. Chinchulkar, "A Study on Identification of Plant Diseases Using Image Processing," in Computer Networks, Big Data and IoT, A.P. Pandian, X. Fernando, and W. Haoxiang, Eds. Singapore: Springer, 2022, vol. 117. [Online]. Available: https://doi.org/10.1007/978-981-19-0898-9_36
J. Liu and X. Wang, "Plant diseases and pests' detection based on deep learning: a review," Plant Methods, vol. 17, p. 22, 2021. [Online]. Available: https://doi.org/10.1186/s13007-021-00722-9
J. Zhang, Y. Huang, R. Pu, P. Gonzalez-Moreno, L. Yuan, K. Wu, and W. Huang, "Monitoring plant diseases and pests through remote sensing technology: A review," Comput. Electron. Agric., vol. 165, Article 104943, 2019.
K. Nagasubramanian, S. Jones, A.K. Singh, et al., "Plant disease identification using explainable 3D deep learning on hyperspectral images," Plant Methods, vol. 15, p. 98, 2019. [Online]. Available: https://doi.org/10.1186/s13007-019-0479-8
N. Kotwaliwale, K. Singh, A. Kalne, S.N. Jha, N. Seth, and A. Kar, "X-ray imaging methods for internal quality evaluation of agricultural produce," J Food Sci Technol., vol. 51, no. 1, pp. 1-15, Jan. 2014. [Online]. Available: https://doi.org/10.1007/s13197-011-0485-y
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, pp. 1-21, 2017. [Online]. Available: https://doi.org/10.3390/s17092022
R.I. Hasan, S.M. Yusuf, and L. Alzubaidi, "Review of the state of the art of deep learning for plant Diseases: A broad analysis and discussion," Plants, vol. 9, pp. 1-25, 2020. [Online]. Available: https://doi.org/10.3390/plants9101302
A. 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," Comput. Electron. Agric., vol. 167, Article 105093, 2019. [Online]. Available: https://doi.org/10.1016/j.compag.2019.105093
A. Khamparia, G. Saini, D. Gupta, A. Khanna, S. Tiwari, and V.H.C. Albuquerque, "Seasonal crops disease prediction and classification using deep convolutional encoder network," Circuits, Systems, and Signal Processing, vol. 39, pp. 818–836, 2019.
R. Karthik, M. Hariharan, S. Anand, P. Mathikshara, A. Johnson, and R. Menaka, "Attention embedded residual CNN for disease detection in tomato leaves" in Applied Soft Computing, vol. 86, 2020.
K. Perveen et al., "Multidimensional Attention-Based CNN Model for Identifying Apple Leaf Disease," Journal of Food Quality, vol. 2023, Article ID 9504186, pp. 1-12, 2023.
H. Alghamdi and T. Turki, "PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features," Agriculture, vol. 13, p. 1072, 2023.
P. Dhiman et al., "PFDI: a precise fruit disease identification model based on context data fusion with faster-CNN in edge computing environment," EURASIP J. Adv. Signal Process., vol. 2023, pp. 1-18, 2023. [Online]. Available: https://doi.org/10.1186/s13634-023-01025-y
D. Gupta and R. Singh, "Disease Detection in Plant using Artificial Neural Network," International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 7, no. VI, pp. 2560-2565, June 2019. [Online]. Available: https://doi.org/10.22214/ijraset.2019.6434
C.U. Kumari, S.J. Prasad, and G. Mounika, "Leaf Disease Detection: Feature Extraction with K-means clustering and Classification with ANN," in 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, pp. 1095-1098. [Online]. Available: https://doi.org/10.1109/ICCMC.2019.8819750
E. Önler, "Feature fusion based artificial neural network model for disease detection of bean leaves," Electronic Research Archive, vol. 31, no. 5, pp. 2409-2427, 2023. [Online]. Available: https://doi.org/10.3934/era.2023122
M. Ayaz et al., "Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk," IEEE Access, vol. 7, pp. 129551–129583, 2019. [Online]. Available: https://doi.org/10.1109/ACCESS.2019.2932609
A. Nawaz et al., "Plant Disease Detection using Internet of Thing (IoT)," International Journal of Advanced Computer Science and Applications, vol. 11, no. 5, 2020.
M. Sowmiya and S. Krishnaveni, "IoT enabled prediction of agriculture's plant disease using improved quantum whale optimization DRDNN approach," Measurement: Sensors, vol. 27, Article 100812, 2023.
Y. Zhao et al., "An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild," Appl. Soft Comput. J., vol. 89, 2020. [Online]. Available: https://doi.org/10.1016/j.asoc.2020.106128
D. R. Wilson and T. R. Martinez, "The need for small learning rates on large problems," in IJCNN'01. International Joint Conference on Neural Networks. Proceedings, Washington, DC, USA, 2001, pp. 115-119 vol.1, https://doi.org/10.1109/IJCNN.2001.939002.
S. Afaq and S. Rao, "Significance of epochs on training a neural network," Int. J. Sci. Technol. Res., vol. 9, no. 06, pp. 485-488.
P. M. Radiuk, "Impact of training set batch size on the performance of convolutional neural networks for diverse datasets," Information Technology and Management Science, vol. 20, no. 1, pp. 20-24, 2017, https://doi.org/10.1515/itms-2017-0003.
D. S. Terzi, "Do different weight initialization strategies have an impact on transfer learning for plant disease detection," August 24, 2023, PREPRINT (Version 1), https://doi.org/10.21203/rs.3.rs-3249850/v1.
