Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard.

Tomas Poblete,Dongryeol Ryu,Samuel Ortega-Farías

doi:10.3390/s18020397

Tomas Poblete, Dongryeol Ryu + Show 1 more

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https://doi.org/10.3390/s18020397

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Abstract

Water stress caused by water scarcity has a negative impact on the wine industry. Several strategies have been implemented for optimizing water application in vineyards. In this regard, midday stem water potential (SWP) and thermal infrared (TIR) imaging for crop water stress index (CWSI) have been used to assess plant water stress on a vine-by-vine basis without considering the spatial variability. Unmanned Aerial Vehicle (UAV)-borne TIR images are used to assess the canopy temperature variability within vineyards that can be related to the vine water status. Nevertheless, when aerial TIR images are captured over canopy, internal shadow canopy pixels cannot be detected, leading to mixed information that negatively impacts the relationship between CWSI and SWP. This study proposes a methodology for automatic coregistration of thermal and multispectral images (ranging between 490 and 900 nm) obtained from a UAV to remove shadow canopy pixels using a modified scale invariant feature transformation (SIFT) computer vision algorithm and Kmeans++ clustering. Our results indicate that our proposed methodology improves the relationship between CWSI and SWP when shadow canopy pixels are removed from a drip-irrigated Cabernet Sauvignon vineyard. In particular, the coefficient of determination (R2) increased from 0.64 to 0.77. In addition, values of the root mean square error (RMSE) and standard error (SE) decreased from 0.2 to 0.1 MPa and 0.24 to 0.16 MPa, respectively. Finally, this study shows that the negative effect of shadow canopy pixels was higher in those vines with water stress compared with well-watered vines.

Highlights

Water availability is a critical limiting factor in the agricultural industry; a wide range of new technologies and strategies have been adopted to optimize the agricultural water consumption [1,2,3,4]
Granier et al [5] argued that the measurements of physiological parameters can provide better information about the whole-plant-level water use with changing atmospheric water demands
Sensors 2018, 18, 397 considerable time when these measurements are extended to cover a large area [13,14]. This limitation has motivated the development of cost- and time-effective alternatives to evaluate plant water status

Summary

Introduction

Water availability is a critical limiting factor in the agricultural industry; a wide range of new technologies and strategies have been adopted to optimize the agricultural water consumption [1,2,3,4]. Granier et al [5] argued that the measurements of physiological parameters can provide better information about the whole-plant-level water use with changing atmospheric water demands. Sensors 2018, 18, 397 considerable time when these measurements are extended to cover a large area [13,14]. This limitation has motivated the development of cost- and time-effective alternatives to evaluate plant water status. Multispectral imagery to capture images at the leaf and canopy levels has been proposed as an effective tool for agricultural applications [15] to indirectly and remotely assess plant water status

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