Mapping wheat response to variations in N, P, Zn, and irrigation using an unmanned aerial vehicle. Issue 21 (2nd November 2018)

• Record Type: Journal Article
• Authors: Latif, Muhammad Ahsan; Cheema, Muhammad Jehanzeb Masud; Saleem, Muhammad Farrukh; Maqsood, Muhammad
• Is Part Of: International journal of remote sensing. Volume 39:Issue 21(2018)
• Page Start: 7172
• Page End: 7188
• Publication Date: 2018-11-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1515509
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The ability to identify the vulnerable regions within the crop fields assist the farmers in executing the counter plans precisely. In this regard, the unmanned aerial vehicles (UAVs) equipped with red-green-blue and multispectral sensors have added a new dimension in precision agriculture for a broad range of applications. In this study, we present the transformation of an off-the-shelf quadrotor platform into an aerial-crop-observer by adding customized payload capable of delivering high-resolution multispectral imagery. The main objective of the study was to aerially quantify the response of wheat crop under the influence of different elements critical to crop health, i.e. nitrogen (N), phosphorus, zinc, irrigation levels (I), and agro-climatological conditions. Two different experimental plots with different varieties of wheat crop were selected for the study. The vegetation indices were derived after all the necessary radiometric and geometrical corrections. The linear estimation models were developed to assess the grain yield, aboveground biomass, and leaf area index. Our results indicate that high-resolution multispectral imagery acquired through lightweight UAVs offers a standing potential for quantifying aerial observations of wheat crop under a variety of field-inputs. The study is characterized through respective soil analysis, applied field inputs, calibration of aerial image sensors, the agro-climatological observations, and derivations of vegetation indices.