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Keywords

unmanned aircraft, grain sorghum. multispectral indices, remote sensing, NDVI, NDRE, SAVI

Abstract

Grain sorghum is an important crop in cropping systems worldwide. Many different genetic lines are tolerant to post-flowering heat and drought stress because they express the “stay-green” trait which causes a delay in senescence patterns. Traditional methods of senescence identification are labor-intensive and time consuming. However, remote sensing is a proposed method of identifying sorghum senescence. A study using small unmanned aircraft systems (sUAS) as a remote sensing platform was conducted in Concordia, KS. Twenty sorghum varieties with 3 replications were sown in a random­ized block design. The aircraft used was a DJI S-1000 equipped with a MicaSense RedEdge 3 multispectral camera. Two successful flights were completed after the flow­ering period (September 13 and October 4, 2018). Subsequent ground-truthed senes­cence ratings were taken on both days, with each leaf of 4 sample plants being assigned a senescence score between 100 and 0 (100 indicating no visible leaf senescence and 0 indicating complete leaf senescence). Data processing was done using Agisoft Photoscan Pro to generate an orthomosaic image and ArcGIS Pro for vegetation index genera­tion and data extraction. Three vegetation indexes (VI) were generated: the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), and soil adjusted vegetation index (SAVI). The NDRE was the only significant VI of the three found to predict whole plant senescence. It also had the strongest correlation coeffi­cient when analyzed with ground-truthed senescence scores. When comparing NDVI, NDRE, and SAVI data, the NDRE index is the best indicator of grain sorghum senes­cence.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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