Abstract
In this review paper, previous and current works for plant leaf disease detection have been studied. The traditional manual visual quality inspection cannot be defined systematically as this method is unpredictable and inconsistent. Moreover, it involves a remarkable amount of expertise in the field of plant disease diagnostics (phytopathology) in addition to the disproportionate processing times. Hence, image processing has been applied for the recognition of plant diseases. This paper has been divided into three main parts. In the first part, a comprehensive review based on algorithms is provided were the major algorithms and works conducted using image processing and artificial intelligence algorithms have been compared. The second part discusses the frameworks and compared the previous works. Then, a comprehensive discussion based on the accuracy of the results was provided. Based on the review conducted, a detailed explanation of the illnesses detection and classification performance is provided. Finally, the findings and challenges in plant leaf detection using image processing are summarized and discussed.
Reference37 articles.
1. S. Bankar, A. Dube, P. Kadam, and D. Sunil, “Plant Disease Detection Techniques Using Canny Edge Detection & Color Histogram in Image Processing,” 2014. [Online]. Available: www.ijcsit.com
2. S. H. Lee, C. S. Chan, S. J. Mayo, and P. Remagnino, “How deep learning extracts and learns leaf features for plant classification,” Pattern Recognit, vol. 71, pp. 1–13, Nov. 2017, doi: 10.1016/j.patcog.2017.05.015.
3. S. A. Tsaftaris, M. Minervini, and H. Scharr, “Machine Learning for Plant Phenotyping Needs Image Processing,” Trends Plant Sci, vol. 21, no. 12, pp. 989–991, Dec. 2016, doi: 10.1016/j.tplants.2016.10.002.
4. A. Fuentes, S. Yoon, and D. S. Park, “Deep Learning-Based Techniques for Plant Diseases Recognition in Real-Field Scenarios,” 2020, pp. 3–14. doi: 10.1007/978-3-030-40605-9_1.
5. J. Li, Y. Mi, G. Li, and Z. Ju, “CNN-Based Facial Expression Recognition from Annotated RGB-D Images for Human–Robot Interaction,” International Journal of Humanoid Robotics, vol. 16, no. 04, p. 1941002, Aug. 2019, doi: 10.1142/S0219843619410020.
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Image Processing of Big Data for Plant Diseases of Four Different Plant Categories;International Journal of Computer Vision and Image Processing;2024-08-29
2. An Accurate Plant Disease Detection Technique Using Machine Learning;EAI Endorsed Transactions on Internet of Things;2024-01-29
3. Leaf Disease Detection using Image Processing;2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS);2023-10-27
4. Comparative Analysis of Algorithms for Cotton Plant Leaf Disease Classification from an Image;2023 IEEE International Carnahan Conference on Security Technology (ICCST);2023-10-11
5. Cotton Leaf Disease Detection Using Artificial Intelligence with Autonomous Alerting System;2023 World Conference on Communication & Computing (WCONF);2023-07-14