Mapping Crop Status From an Unmanned Aerial Vehicle for Precision Agriculture Applications

Abstract

ABSTRACT:Remote sensing system mounted on unmanned aerial vehicle (UAV) could provide a complementary means to the conventional satellite and aerial remote sensing solutions especially for the applications of precision agriculture. UAV remote sensing offers a great flexibility to quickly acquire field data in sufficient spatial and spectral resolution at low cost. However a major problem of UAV is the high instability due to the low-end equipments and difficult environment situation, and this leads to image sensor being mostly operated under a highly uncertain configuration. Thus UAV images exhibit considerable derivation in spatial orientation, large geometric and spectral distortion, and low signal-to-noise ratio (SNR). To achieve the objectives of agricultural mapping from UAV, we apply a micro-helicopter UAV with a multiple spectral camera mounted and develop a framework to process UAV images. A very important processing is to generate mosaic image which can be aligned with maps for later GIS integration. With appropriate geometric calibration applied, we first decompose a homography of consecutive image pairs into a rotational component and a simple perspective component, and apply a linear interpolation to the angle of the rotational component, followed by a linear matrix interpolation operator to the perspective component, and this results in an equivalent transformation but ensures a smooth evolution between two images. Lastly to demonstrate the potential of UAV images to precision agriculture application, we perform spectral processing to derive vegetation indices (VIs) maps of crop, and also show the comparison with satellite imagery. Through this paper, we demonstrate that it is highly feasible to generate quantitative mapping products such as crop stress maps from UAV images, and suggest that UAV remote sensing is very valuable for the applications of precision agriculture.

Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images

“…UAV platforms (multi-rotors, swinglet, model helicopters, etc. ), coupled with imaging, ranging, and positioning sensors, are able to collect multispectral imagery at cm-level resolution and offer great possibilities in the precision farming domain [7][8][9][10], agriculture and forestry management [11,12], and geosciences [13]. UAVs allow us to perform many interesting and quantitative observations at better spatial and temporal resolution and lower costs with respect to airborne platforms or satellites (see Table 1).…”

Big Earth data analytics: a survey

“…Measuring site-specific environmental attributes of agricultural land (Bendig, Bolten, & Bareth, 2012;Guo, Kujirai, & Watanabe, 2012;Primicerio et al, 2012;Candiago, Remondino, De Giglio, Dubbini, & Gattelli, 2015;Kross, McNairn, Lapen, Sunohara, & Champagne, 2015;Whitcraft, Becker-Reshef, & Justice, 2015) Monitoring crop growth and health from space and air (Nelson, 2017) Hazard and risk Quantifying hazard, exposure and vulnerability elements attributable to property damage or loss (Ezequiel et al, 2014;Plank, 2014;Skakun, Kussul, Shelestov, & Kussul, 2014;Mas et al, 2015;Pradhan, Tehrany, & Jebur, 2016;Yamazaki & Liu, 2016) Rescue and response (Kavanaugh et al, the data dimension to monitor the dynamics of Earth surface (Asner et al, 2012), including drought (Zhang & Jia, 2013), water vapor (Liu et al, 2013), land surface temperature (Maimaitiyiming et al, 2014), and vegetation (Guay et al, 2014). These data can be used to analyze climate change factors and their spatiotemporal patterns (Faghmous & Kumar, 2014).…”

“…UAV platforms (multi-rotors, swinglet, model helicopters, etc. ), coupled with imaging, ranging, and positioning sensors, are able to collect multispectral imagery at cm-level resolution and offer great possibilities in the precision farming domain (Bendig et al, 2012;Guo et al, 2012;Primicerio et al, 2012).…”

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

“…Although a conventional photographic RGB camera can be used to generate topographic surveys and orthoimages, it takes a multispectral camera covering the visible and IR bands to make differentiating analyses of the landcover using standardised methods of image processing with a prior knowledge of the corresponding spectral signatures. Hence, the combination of modern high spatial resolution and multispectral band sensors offers the possibility to study crops for precision agriculture (Guo, Kujirai, & Watanabe, 2012;Primicerio et al, 2012).…”