Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat

2013 ◽  
Vol 15 (1) ◽  
pp. 44-56 ◽  
Author(s):  
D. Gómez-Candón ◽  
A. I. De Castro ◽  
F. López-Granados
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Aerial Vehicle

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

A flexible unmanned aerial vehicle for precision agriculture

2012 ◽  
Vol 13 (4) ◽  
pp. 517-523 ◽  
Author(s):  
Jacopo Primicerio ◽  
Salvatore Filippo Di Gennaro ◽  
Edoardo Fiorillo ◽  
Lorenzo Genesio ◽  
Emanuele Lugato ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Aerial Vehicle

scholarly journals Measurement of Cotton Canopy Temperature Using Radiometric Thermal Sensor Mounted on the Unmanned Aerial Vehicle (UAV)

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Anjin Chang ◽  
Jinha Jung ◽  
Murilo M. Maeda ◽  
Juan A. Landivar ◽  
Henrique D. R. Carvalho ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Water Status ◽  
Canopy Temperature ◽  
Sensor Technology ◽  
Stress Index ◽  
Cotton Field ◽  
Radiometric Calibration ◽  
Great Opportunity ◽  
Aerial Vehicle

Canopy temperature is an important variable directly linked to a plant’s water status. Recent advances in Unmanned Aerial Vehicle (UAV) and sensor technology provides a great opportunity to obtain high-quality imagery for crop monitoring and high-throughput phenotyping (HTP) applications. In this study, a UAV-based thermal system was developed to directly measure canopy temperature, skipping the traditional radiometric calibration process which is time-consuming and complicates data processing. Raw thermal imagery collected over a cotton field was converted to surface temperature using the Software Development Kit (SDK) provided by the sensor company. Canopy temperature map was generated using Structure from Motion (SfM), and Thermal Stress Index (TSI) was calculated for the test site. UAV temperature measurements were compared to ground measurements acquired by net radiometers and thermocouples. Temperature differences between UAV and ground measurements were less than 5%, and UAV measurements proved to be more stable. The proposed UAV system was successful in showing temperature differences between the cotton genotype. In conclusion, the system described in this study could possibly be used to monitor crop water status in a field setting, which should prove helpful for precision agriculture and crop research.

scholarly journals Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7098
Author(s):  
Xiongzhe Han ◽  
J. Alex Thomasson ◽  
Vaishali Swaminathan ◽  
Tianyi Wang ◽  
Jeffrey Siegfried ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Multiple Linear Regression Model ◽  
Thermal Image ◽  
P Value ◽  
Stress Monitoring ◽  
Ambient Conditions ◽  
Calibration Methods ◽  
Aerial Vehicle ◽  
Temperature Controlled

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

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