Automated Wheat Stem Rust Detection using Computer Vision
2023-05
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Automated Wheat Stem Rust Detection using Computer Vision
Authors
Published Date
2023-05
Publisher
Type
Thesis or Dissertation
Abstract
Wheat is one of the most important cereal crops, contributing significantly to the financial economy and food sources. Currently, the direct consumption of wheat amounts to about 41%. Additionally, in 2019 alone, the global trade value of wheat was about $39.6 billion. Hence, the protection of the yield of such crops from diseases is of immense importance. Stem rust is a fungal disease that attacks cereal crops. In particular, it is a common disease that occurs in wheat and destroys 50 to 70% of the yield if left unchecked. The loss of yield would in turn affect the economy and food consumption. Thus, there is a need to detect the outbreak early to apply fungicide treatment to the field. The traditional approach for detection involves experts inspecting the fields visually and grading them for stem rust which is a time-consuming process for a large field and can also be affected by human errors. Hence, an automated approach to the grading process would help solve such problems. The availability of an automated grading process will allow mobile robots, popularly being used for activities like irrigation, seed sowing, and precision agriculture to rapidly perform grading and alert the experts in case of detected stem rust. The alert through the automated detection would in turn lead to a timely application of fungicide for preventing the spread of stem rust in an efficient manner. The thesis focuses on formulating the wheat rust grading as a multi-class classification problem and demonstrating the effectiveness of the visual attention approach for solving it. The thesis also presents the first RGB field dataset with labels from experts for the development of automated stem rust grading approaches. The proposed approach was developed and evaluated on the presented dataset and shows the ability to distinguish between different intensities of stem rust with 86% accuracy. The reliability of the network is also validated qualitatively through attention maps where the visual attention approach shows interpretable focus areas compared to traditional detection approaches which fail to identify the general presence area of stem rust.
Description
University of Minnesota M.S. thesis. May 2023. Major: Computer Science. Advisor: Volkan Isler. 1 computer file (PDF); ix, 49 pages.
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Mahesh, Rahul Moorthy. (2023). Automated Wheat Stem Rust Detection using Computer Vision. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/256952.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.