Wheat stem rust disease detection using drone hyperspectral imaging (Conference Presentation)

Ce Yang; Jaafar Abdulridha; An Min; Matthew Rouse; Shahryar Kianian; Volkan Isler

doi:10.1117/12.2665144

13 June 2023 Wheat stem rust disease detection using drone hyperspectral imaging (Conference Presentation)

Ce Yang, Jaafar Abdulridha, An Min, Matthew Rouse, Shahryar Kianian, Volkan Isler

Proceedings Volume PC12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII; PC1253901 (2023) https://doi.org/10.1117/12.2665144
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Drone hyper-spectral imaging was used in this study to evaluate the severity of wheat stem rust disease in a disease trial containing 960 plots. Quadratic data analysis and random forest classifier, decision tree classification, and support vector machine were applied to select the wavelengths and spectral vegetation indices (SVIs). The trial plots were divided into four levels based on ground truth: class 0 (healthy, scored 0), class 1 (mildly diseased, scored 1-15), class 2 (moderately diseased, scored 16-34) and class 3 (severely diseased, scored 35+). The RFC method achieved the highest classification accuracy, which was 85% for overall classification. The RFC method with selected SVIs, and the accuracy ranged between 82%-96%. Green NDVI, Photochemical Reflectance Index, Red-Edge Vegetation Stress Index and Chlorophyll Green were selected from 14 SVIs. It is possible to build a new inexpensive multispectral imaging system for stem rust disease detection.

Conference Presentation

Citation Download Citation

Ce Yang, Jaafar Abdulridha, An Min, Matthew Rouse, Shahryar Kianian, and Volkan Isler "Wheat stem rust disease detection using drone hyperspectral imaging (Conference Presentation)", Proc. SPIE PC12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII, PC1253901 (13 June 2023); https://doi.org/10.1117/12.2665144

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KEYWORDS

Hyperspectral imaging

Cameras

Data analysis

Unmanned aerial vehicles

Vegetation

Binary data

Diagnostics

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