R. Ballesteros

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing

There are many aspects of crop management that might benefit from aerial observation. Unmanned aerial vehicle (UAV) platforms are evolving rapidly both technically and with regard to regulations. The purpose of this study was to acquire images with conventional RGB cameras using UAVs and process them to obtain geo-referenced ortho-images with the aim of characterizing the main plant growth parameters required in the management of irrigated crops under semi-arid conditions. The paper is in two parts, the first describes the image acquisition and processing procedures, and the second applies the proposed methodology to a case study. In the first part of the paper, the type of UAV utilized is described. It was a vertical take-off and landing quadracopter aircraft with a conventional RGB compact digital camera. Other types of on-board sensors are also described, such as near-infrared sensors and thermal sensors, and the problems of using these types of expensive sensor is discussed. In addition, software developed by the authors for photogrammetry processing, and information extraction from the geomatic products are described and analysed for agronomic applications. This software can also be used in other applications. To obtain agronomic parameters, different strategies were analysed, such as the use of computer vision for canopy cover extraction, as well as the use of vegetation indices derived from the visible spectrum, as a proper solution when very-high resolution imagery is available. The use of high-resolution images obtained with UAVs together with proper treatment might be considered a useful tool for precision in monitoring crop growth and development, advising farmers on water requirements, yield production, weed and insect infestations, among others. More studies, focusing on the calibration and validation of these relationships in other crops are required.

Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Leaf area index (LAI), green canopy cover (GCC), and canopy volume (V) are associated with grape vigor, quality, and yield. Thus, analyzing these parameters throughout the growing season may help optimize site-specific management of grape vineyards. Because direct measurements of LAI are destructive, tedious, and not repeatable on the same vine, developing and validating nondestructive methods to estimate LAI are essential. Canopy pattern is characterized by GCC and V, which can be measured using aerial observation. The purpose of this study was to characterize growth parameters, such as LAI, GCC, and V, of irrigated and rainfed Vitis vinifera L. under semiarid conditions on two different vineyards using aerial images from an unmanned aerial vehicle. The relationships between GCC versus LAI and V versus LAI were calculated and validated. Relationships between LAI and the other parameters depend on canopy management, training system, and pruning practices. Relationships between LAI and growing degree days (GDD) and V and GDD were also obtained to determine the canopy structure pattern during the growing season. Exponential polynomial and second-order polynomial models showed the best fit for describing the relationships between GCC and GDD and between V and GDD, respectively, for Airén variety.

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain

Leaf area index (LAI) is involved in biological, environmental and physiological processes, which are related to photosynthesis, transpiration, interception of radiation and energy balance. Thus, most crop models use LAI as a key feature to characterize the growth and development of crops. However, direct measures of LAI are destructive and tedious so that samplings can seldom be repeated in time and in space. Green canopy cover (GCC) is directly involved in crop growth and development. GCC estimation can benefit from aerial observation, as it can be measured by using image analysis or estimated by obtaining different vegetation indices. The main purpose of the second part of this paper was to study the relationships between GCC and LAI by using aerial images from UAVs in order to characterize crop growth. Also, the relationships between GCC and a vegetation index based on the visible spectrum was calibrated and validated. Relationships between LAI and GCC, growing degree days (GDD) and GCC and GDD and LAI were calibrated and validated for maize and onion crops with proper fitting. Visible atmospherically resistant index also appears to be a sensitive indicator to different growing stages and could generally be applied to any field crop. To apply this methodology, GCC and LAI relationships must be calibrated for many other crops in different irrigable areas. In addition, the cost of the UAV is expected to decrease while autonomy increases through improved battery life and reductions in the weight of on-board sensors.

Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture

The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to the low load capacity of the UAV they need to mount light, uncooled thermal cameras, where the microbolometer is not stabilized to a constant temperature. This makes the camera precision low for many applications. Additionally, the low contrast of the thermal images makes the photogrammetry process inaccurate, which result in large errors in the generation of orthoimages. In this research, we propose the use of new calibration algorithms, based on neural networks, which consider the sensor temperature and the digital response of the microbolometer as input data. In addition, we evaluate the use of the Wallis filter for improving the quality of the photogrammetry process using structure from motion software. With the proposed calibration algorithm, the measurement accuracy increased from 3.55 °C with the original camera configuration to 1.37 °C. The implementation of the Wallis filter increases the number of tie-point from 58,000 to 110,000 and decreases the total positing error from 7.1 m to 1.3 m.

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

Onion biomass monitoring using UAV-based RGB imaging

Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination (

CARACTERIZACIÓN DEL CRECIMIENTO DE UN CULTIVO DE MAÍZ REGADO EN UNA ZONA SEMIÁRIDA MEDIANTE EL EMPLEO DE IMÁGENES AÉREAS DE ALTA RESOLUCIÓN

{\text{R}}_{\text{adj}}^2

Approximate georeferencing and automatic blurred image detection to reduce the costs of UAV use in environmental and agricultural applications

Radj2) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.