Calibration and Image Processing of Aerial Thermal Image for UAV Application in Crop Water Stress Estimation

Abstract

Crop monitoring is a very important area of precision agriculture and smart farming. Through an accurate monitoring, it is possible to more efficiently manage the irrigation, fertilization, and pest control. In this study, we propose aerial thermal image calibration method and thermal image processing techniques to analyze the water stress level of fruit trees under different irrigation conditions. The calibration was performed using Gaussian process regression, and it was demonstrated as an appropriate regression method because it satisfied all requirements including the residuals’ normality, independence, and homoscedasticity. In addition, an appropriate image processing technique was necessary to selectively extract only the canopy temperature from the aerial thermal images, while excluding irrelevant elements such as the soil and other objects. For the image processing techniques, three methods (Gaussian mixture model, Otsu binarization algorithm, and Otsu binarization algorithm after Gaussian blurring) were employed. The Gaussian mixture model provided the highest accuracy and stable results for the extraction of the canopy temperature. After the aerial thermal images were subjected to calibration and image processing, the degree above nonstressed canopy (DANS) water stress index was calculated for the fruit trees under different water supply conditions. The distribution of the DANS water stress index was similar to the distribution of the canopy temperature and inversely proportional to the amount of supplied water content. Therefore, we expect that the DANS water stress index, calculated using the calibration and image processing techniques proposed in this study, can be a reliable measure for the estimation of the water stress of crops for the application of aerial infrared techniques to remote sensing.

High-throughput field crop phenotyping: current status and challenges

“…As a result, consistent and repeatable measurements over crop canopies are difficult ( Perich et al 2020 ). However, several ideas have been proposed by researchers to achieve reliable CT measurements for maize ( Zhang et al 2019 ), fruit trees ( Han et al 2021 ), wheat ( Perich et al 2020 ), soybean ( Crusiol et al 2020 ), and barley ( Hoffmann et al 2016 ). For example, Perich et al (2020) used the heritability of CT to identify the optimal timing of the measurement.…”

Feasibility and accuracy assessment of UAV, aircraft, and satellite-based remote sensing for irrigation management using canopy temperature and NDVI.

“…However, UAV imagery provides elasticity in operations; users can perform multitemporal and custom-defined campaigns. Low-altitude UAV flights are easy and safer to conduct, provide a very high spatial resolution of centimeters level and avoid the majority of atmospheric interferences and clouds [Han et al, 2021;White et al, 2012;Zarco-Tejada et al, 2013]. UAVs have widespread implementation and are suitable for small farms but have weaknesses like limited payload capacity, short flight duration, technical complexity, and little spatial coverage.…”

Integrating Drone Technology into an Innovative Agrometeorological Methodology for the Precise and Real-Time Estimation of Crop Water Requirements

“…The direct estimation of the CWSI is based on spatial data that is remote and directly from the field and is based mainly on the infrared temperature of the crop canopy. Even in the recent (2021) literature, but also in previous years, the calculation of the CWSI with direct spatial measurements of the temperature of the crop canopy has never been proposed or adapted [37][38][39][40][41][42][43]. Such measurements are constantly achieved indirectly through thermal images.…”