Spatial Analysis Of Multispectral And Thermal Imagery From Multiple Platforms (Autonomous Air And Ground Sensing Systems For Agricultural Optimization And Phenotyping III

(Vol. 10664, P. 106640T). International Society For Optics And Photonics.)

Abstract: The possibility application of multispectral imagery using Unmanned Aerail Vehicle (UAV) as platform to predict Brix value in sugarcane field was evaluated. Total 4 flight missions were conducted in November 2017 using rotate- wing UAV and a 5- band MicaSen- RedEdge camera sensor to fly in the afternoon time (around 2:00pm) over an experiment trail which consists of 4 varieties (KK3, K88-92, UT84-12, KK06-501). The sugarcane is new planted, in its 10-month-old with the range of brix 15-21 0 Brix, and grown under rainfed. After each flight, Brix measurement was done using refractometer with 2 random juice samples in each plot, total 32 samples in this study. Acquired Images were processed in Pix4D mapper software to create reflectance map of each band (Blue, Greem, Red, NIR, and RedEdge). Matlab was used to compute vegetation indices (RVI, NDVI, GNDVI, SRPIb, CIgreen, and CIrededge). Then the calibration equations are determined using simple regression between each vegetation indices and brix value. The result showed that GNDVI has the best correlation with Brix value, Standard Erro (SE) 0.45 0 Brix and R2
0.86, while the rest vegetation indices have higher SE 0.51-1.13 0 Brix. From preliminary result, it is possible to predict Brix value in the field from spectra of multispectral camera mounted on UAV. The accuracy of model can be improved by adding more data from various concerning environment conditions such as light and temperature, and more range of brix data because datas used in this study are still in early harvest season. Airborne and satellite remote sensing can potentially be used to model crop characteristics. However, satellite imagery usually exhibit low spatial and temporal resolutions, and manned aircraft imagery, despite improved resolutions, is not cost-effective. Recent developments in UAV remote sensing have allowed for imagery at improved spatial resolutions relative to satellites and at a fraction of the cost relative to manned aircraft. Furthermore, UAVs offer potential advantages over proximal soil sensors (i.e. EM-38) in terms of in-season decision making. However, it is unclear at this point whether these benefits translate to higher quality information. This question has relevance within fields that exhibit contrasting environments, such as soil spatial variability. Therefore, the objectives of this paper were twofold: 1) to quantify improvements in UAV-based plant (cotton) modelling relative to proximal sensing (i.e. EM-38), manned aircraft, and satellites (Landsat 8); and 2) to determine how such modeling can be affected by soil spatial variability. Results indicate that UAVs show higher nugget/sill ratios and larger ranges than manned aircraft and satellites. These results have implications for predicting agronomic variables (i.e. yield, plant height), as well as soil/plant sampling.Abstract: The possibility application of multispectral imagery using Unmanned Aerail Vehicle (UAV) as platform to predict Brix value in sugarcane field was evaluated. Total 4 flight missions were conducted in November 2017 using rotate- wing UAV and a 5- band MicaSen- RedEdge camera sensor to fly in the afternoon time (around 2:00pm) over an experiment trail which consists of 4 varieties (KK3, K88-92, UT84-12, KK06-501). The sugarcane is new planted, in its 10-month-old with the range of brix 15-21 0 Brix, and grown under rainfed. After each flight, Brix measurement was done using refractometer with 2 random juice samples in each plot, total 32 samples in this study. Acquired Images were processed in Pix4D mapper software to create reflectance map of each band (Blue, Greem, Red, NIR, and RedEdge). Matlab was used to compute vegetation indices (RVI, NDVI, GNDVI, SRPIb, CIgreen, and CIrededge). Then the calibration equations are determined using simple regression between each vegetation indices and brix value. The result showed that GNDVI has the best correlation with Brix value, Standard Erro (SE) 0.45 0 Brix and R2
0.86, while the rest vegetation indices have higher SE 0.51-1.13 0 Brix. From preliminary result, it is possible to predict Brix value in the field from spectra of multispectral camera mounted on UAV. The accuracy of model can be improved by adding more data from various concerning environment conditions such as light and temperature, and more range of brix data because datas used in this study are still in early harvest season.

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Authors: Rouze, G., Neely, H., Morgan, C., & Yang, C.

Associations: Texas A&M Univ. (United States) USDA-Agricultural Research Service (United States)

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