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.
In order to address the worldwide growing demand for food, agriculture is facing certain challenges and limitations. One of the important threats limiting crop productivity is salinity. Identifying salt tolerate varieties is crucial to mitigate the negative effects of this abiotic stress in agricultural production systems. Traditional measurement methods of this stress, such as biomass retention, are labor intensive, environmentally influenced, and often poorly correlated to salinity stress alone. In this study, hyperspectral imaging, as a non-destructive and rapid method, was utilized to expedite the process of identifying relatively the most salt tolerant line among four wheat lines including Triticum aestivum var. Kharchia, T. aestivum var. Chinese Spring, (Ae. columnaris) T. aestivum var. Chinese Spring, and (Ae. speltoides) T. aestivum var. Chinese Spring. To examine the possibility of early detection of a salt tolerant line, image acquisition was started one day after stress induction and continued on three, seven, and 12 days after adding salt. Simplex volume maximization (SiVM) method was deployed to detect superior wheat lines in response to salt stress. The results of analyzing images taken as soon as one day after salt induction revealed that Kharchia and (columnaris)Chinese Spring are the most tolerant wheat lines, while (speltoides) Chinese Spring was a moderately susceptible, and Chinese Spring was a relatively susceptible line to salt stress. These results were confirmed with the measuring biomass performed several weeks later.
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