Mahesh, Rahul Moorthy2023-09-192023-09-192023-05https://hdl.handle.net/11299/256952University of Minnesota M.S. thesis. May 2023. Major: Computer Science. Advisor: Volkan Isler. 1 computer file (PDF); ix, 49 pages.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.enComputer VisionDisease identificationMulti-class classificationVisual attentionWheat stem rust gradingThesis or Dissertation