Comparative analysis of UAV RS and traditional cotton leaf falling evaluation methods

Yuzi Zhang, Wenhua Li, WeiXu Ran, Jian Gu, Yubin Lan

Abstract


Abstract: Evaluating the spraying effect of defoliant is necessary condition to facilitate mechanized cotton harvesting.  However, traditional method to evaluate the effect of spraying defoliation by manually calculating the number of cotton defoliation will produce large statistical error and increase the labor cost.  Low altitude remote sensing provides accurate and timely estimation of biophysical parameters, such as leaf area index (LAI), crop growth and plant biomass.  The objective of this work was to discover a high-precision evaluation way of spraying cotton defoliating by comparing the multispectral remote sensing evaluation method with the traditional evaluation method.  UAV multispectral images were collected in a cotton field ofYellowRiver Basin inChina, and the images in five narrow bands included Red, Blue, Green, Near infrared (NIR) and Red Edge.  The processing of multispectral images was performed in the Python environment, NDVI images and GNDVI images were created.  Based on this, the NDVI data, GNDVI data and manual statistical data were compared.  The results show that compared with traditional methods, the multispectral remote sensing evaluation method can be more accurate and effective in evaluating the spraying effect of cotton defoliant.

Keywords: multispectral remote sensing, defoliation, evaluation, precision agriculture UAV

DOI: 10.33440/j.ijpaa.20210402.178

 

Citation: Zhang Y Z, Li W H, Ran W X, Gu J, Lan Y B.  Comparative analysis of UAV RS and traditional cotton leaf falling evaluation methods.  Int J Precis Agric Aviat, 2021; 4(2): 40–44.


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