Exploring the potential of UAV imagery for variable rate spraying in cotton defoliation application
Abstract
Defoliants spraying is necessary to facilitate mechanized cotton harvesting. However, traditional uniform spraying usually decreases the defoliation effects and increases the costs. In order to address this problem, a variable rate spraying strategy was designed in the context of cotton defoliation. UAV multispectral imagery was collected in a cotton filed ofYellowRiver BasininChina, and the imagery was mosaicked into an ortho-photo. The indexes of NDVI and GNDVI were calculated, and the index maps were generated and later transformed into grid maps. A plant protection UAV was used for variable rate spraying, with the chemical dose proposed by the grid maps. Ground investigation before and after the spraying were conducted carefully to verify the defoliation effects. Investigation results showed that the proposed variable rate spraying strategy yielded satisfactory results with less defoliants, which demonstrated that this technology has potential in the cotton defoliation applications.
Keywords: multispectral remote sensing, cotton, defoliation, UAV
DOI:Â 10.33440/j.ijpaa.20190201.0018
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Citation: Yi L L, Lan Y B, Kong H, Kong F X, Huang H S, Han X.  Exploring the potential of UAV imagery for variable rate spraying in cotton defoliation application.  Int J Precis Agric Aviat, 2019; 2(1): 42–45.
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Xiao, Q., et al. Effect of aviation spray adjuvants on defoliant droplet deposition and cotton defoliation efficacy sprayed by unmanned aerial vehicles. Journal of Agronomy, 2019; 9: p. 217.
Lan, Y., et al. Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and Electronics in Agriculture, 2010; 74: p. 34-38.
Huang, H., et al. A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PloS One, 2018; 13(4): p. e0196302.
Pérez-Ortiz, M., et al. A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing, 2015; 37: p. 533-544.
de Castro, A., et al. An Automatic random Forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sensing, 2018; 10(3): p. 285
Tamouridou, A. A., et al. Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery. Sensors (Basel, Switzerland), 2017; 17(10): p. 2307
Albetis, J., et al. Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sensing, 2017; 9(4): p. 308
Alexandridis, T. K., et al. Novelty detection classifiers in weed mapping: silybum marianum detection on UAV multispectral images. Sensors (Basel, Switzerland), 2017; 17(9): p. 2007
Castaldi, F., et al. Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agriculture, 2017; 18(1): p. 76–94.
López-Granados, F., et al. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 2016; 17(2): p. 183–199.
Gitelson, A., Merzyak, M. N. and Lichtenthaler, H. K. Detection of red-edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 1996; 148: 501–508. doi: 10.1016/S0176-1617(96)80285-9
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