No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Validation and Calibration of a High Resolution Sensor in Unmanned Aerial Vehicles for Producing Images in the IR Range Utilizable in Precision Agriculture
... When infrared absorbers (700 to 2500 nm) are utilized, long-wave radiation is reduced, and heat loss is minimized [7][8][9]. When longwave radiation (2500-40,000 nm) absorbers are employed, the greenhouse's components and plants lose less heat [22,23]. Surfactants reduce the surface tension of water, which disperses condensation, whereas antistatic compounds reduce the tendency for dust to accumulate on plastic sheets. ...
This review aims to provide a comprehensive overview of nano-and microscopic materials that can provide thermal radiation insulation without reducing visible light transmittance, thereby reducing heat loss and conserving energy in greenhouses. We also reviewed the radial and thermal properties of greenhouse covering materials. Fillers, colorants, reinforcers, and additives, as well as glass, plastic film, and plastic sheet materials, were discussed. Additionally, by searching for keywords like insulation film, insulation agent, and infrared insulation, compounds based on graphene and fullerene as well as phase transition materials (PCMs) that may be used for radiation insulation, we proposed their potential use in greenhouse covers. They can be divided into semi-transparent photovoltaic (PV) materials, zinc oxide-based film fillers, and silica filter films. We discussed the radiation heat insulation and light transmission characteristics of these materials. Nano-synthesis techniques were also investigated. Based on latest advances in the literature, future developments in the micro-and macroscale synthesis of nanomaterials will enable additional innovations in covering materials for greenhouse structures. A limiting factor, though, was the high sensitivity of PVs to external climatic and meteorological variables. The ability of materials used to make greenhouse covers to control the microclimate, reduce CO 2 emissions, use less energy, and increase agricultural productivity, however, cannot be disputed. Similar to this, a thorough examination of the uses of various greenhouse technologies reveals that the advancements also have financial advantages, particularly in terms of reducing greenhouse heating and cooling expenses. The PCMs, which decreased greenhouse-operating costs by maintaining constant ambient temperatures, provide ample evidence of this.
... In general, there are many camera calibration techniques in the literature, but in our sUAV based surveillance systems, only limited studies have involved images acquired on sUAV [20]. We cite in particular the techniques that have been explored for short object distances in [21][22][23][24][25] and long object distance in [26] and [27]. In most cases, the camera calibration procedure is done in a laboratory condition to estimate the camera parameters as the prerequisite through close-range calibration. ...
In typical intelligence surveillance and reconnaissance (ISR) missions, persistent surveillance is commonly defined as the exercise of automatic intelligence discovery by monitoring a wide area coverage for hours of operation at a high altitude leveraging aerial platforms (manned or unmanned). The platform can be large enough to carry a matrix of high-resolution sensors and a rack of high-performance computing equipment to process in real-time all sensors' feeds. With the current ISR growing in capability, engineering and optics-based aerial surveillance to find a suitable design solution became a design challenge. More onboard processing is desired for an increasing fidelity/resolution sensors' feed, while matching a constraining SWaP (size, weight, and power) budget requirements in a bandwidthconstrained operating theatre. The advent in small unmanned aerial vehicle (sUAV) technology, able to carry sophisticated optics payloads and to take aerial images from strategic viewpoints has become unavoidable in nowadays battlespace contributing in moving forward the ISR capabilities. The constrained on-board processing power in addition to the strict limit in the flying time of sUAV are amongst the serious challenges to overcome to enable cost-effective persistent surveillance based on sUAV platforms. All previous examples show that tailoring the sensors to match the platforms' environment is a challenging endeavour and therefore architects have shifted their design methodology to be based on hardware and software open architectures as a centrepiece of their approach in building cost-effective surveillance solution design. This chapter is a brief introduction to hardware and software building blocks for developing persistent surveillance systems. In our context, the focus is in particular on Electro-Optic (EO, visual spectrum) and Infrared (IR) integrated solutions leveraging computer vision techniques for surveillance missions.
... The test zone had the coordinates 4.7110 • N, 74.0721 • W. It is located in a tropical zone where there are no seasons. Nevertheless, there is an eight-month rainy season with clouds and high wind speed [21]. Hence, the use of 2 k method output parameters such as attitude (pitch, roll, and yaw), height, airspeed, rate of climb, and throttle is discussed in this article using a telemetry link (MavLink) and a dataflash log to record and send data in real time to a GCS (ground control station). ...
This paper presents an analysis of a Total Energy Control System (TECS) introduced by Lambregts to control unmanned aerial vehicle (UAV) velocity and altitude by using the total energy distribution. Furthermore, an extended Kalman filter (EKF) approach was used to predict aircraft response in terms of angular rates and linear acceleration during a test flight campaign. From both approaches, state equations were obtained to model the aircraft using Matlab-Simulink. From an aerodynamic study, airplane characteristics were obtained in terms of non-dimensional derivatives and compared to those obtained from the experimental methods. It was determined that TECS approach was very accurate; however, disturbance errors could be decreased by adjusting some model parameters. On the other hand, it was difficult to obtain a real estimation from the EKF method due to the presence of turbulence during flight and the relatively low inertia of the scale model. Dynamic characteristics were validated using a low-cost inertial sensor that cab be easily integrated in UAV platforms. The gathered data can be used to predict model characteristics by integrating the information into flight simulators for future design development.
... The camera calibration process often uses close-range calibration in laboratory condition to acquire the camera parameters as the prerequisite for the mapping process in an image mapping software. There are many recent studies on calibration techniques that have been done for short object distance such as by Sauerbier et al. (2011), Chiang et al. (2012, Tahar (2012), Deng and Li (2015), Jimenez and Agudelo (2015), and Zhou and Liu (2015). On the other hand, research on camera calibration for long object distance has also been done by Mohamed and Klaus-Peter (1999), Liu et al. (2011), Rehak et al. (2013), and Skaloud et al. (2014. ...
Unmanned Aerial Vehicles (UAVs) can be used to acquire highly accurate data in deformation survey, whereby low-cost digital cameras are commonly used in the UAV mapping. Thus, camera calibration is considered important in obtaining high-accuracy UAV mapping using low-cost digital cameras. The main focus of this study was to calibrate the UAV camera at different camera distances and check the measurement accuracy. The scope of this study included camera calibration in the laboratory and on the field, and the UAV image mapping accuracy assessment used calibration parameters of different camera distances. The camera distances used for the image calibration acquisition and mapping accuracy assessment were 1.5 metres in the laboratory, and 15 and
25 metres on the field using a Sony NEX6 digital camera. A large calibration field and a portable calibration frame were used as the tools for the camera calibration and for checking the accuracy of the measurement at different camera distances. Bundle adjustment
concept was applied in Australis software to perform the camera calibration and accuracy assessment. The results showed that the
camera distance at 25 metres is the optimum object distance as this is the best accuracy obtained from the laboratory as well as outdoor mapping. In conclusion, the camera calibration at several camera distances should be applied to acquire better accuracy in
mapping and the best camera parameter for the UAV image mapping should be selected for highly accurate mapping measurement.
... Complex maneuvers can be replicated and response can be recorded. Wind tunnels test and CFD methods present limitations on this kind of maneuvers representation [12]. All gathered information can support flight simulators design. ...
The objective of the project was to determine if the accuracy of the estimated data for lateral motion could be increased by the use a priori model information during the system identification multisine maneuver design process. For this purpose, a linear aircraft model was used which contains a-priori information in terms of aerodynamic derivatives. A-priori Information allowed us to obtain a non-uniform power spectrum used in the design of the input signal that excited the ailerons and rudder. Three cases were analyzed for comparison. The first case was obtained with a uniform power spectrum distribution without a-priori knowledge implementation. The second case used the a-priori model state matrix information to determine dominant frequencies of the system and assigned more power to them. The third case a non-uniform optimized power spectrum was determined by the use of a genetic algorithm. The Relative Peak Factor was used as the decision variable to increase the efficiency of the design. A-priori knowledge was introducing by using Marchand methodology. The obtained maneuvers sets were used to excite the aircraft model. The system response was recorded and used to perform a parameter estimation for each case. The results were compared in terms of the accuracy.
... The acquired aerial images can help farmers to evaluate the crop growth status, while providing useful information about canopy greenness, leaf area, and water stress assessment, as well as about various geographical conditions such as crop areas, digital surface models (DSM), and depth contour lines (Jimenez and Agudelo, 2015). Recently, many research results for agricultural applications that used aerial images obtained from UAVs have been reported, such as the monitoring of early stage crop growth (Torres-Sánchez et al., 2014) and the development of a weed detection method (Peña et al., 2013). ...
Purpose: The overall objective of this study was to evaluate the vegetation fraction of soybeans, grown under different cropping conditions using an unmanned aerial vehicle (UAV) equipped with a red, green, and blue (RGB) camera. Methods: Test plots were prepared based on different cropping treatments, i.e., soybean single-cropping, with and without herbicide application and soybean and barley-cover cropping, with and without herbicide application. The UAV flights were manually controlled using a remote flight controller on the ground, with 2.4 GHz radio frequency communication. For image pre-processing, the acquired images were pre-treated and georeferenced using a fisheye distortion removal function, and ground control points were collected using Google Maps. Tarpaulin panels of different colors were used to calibrate the multi-temporal images by converting the RGB digital number values into the RGB reflectance spectrum, utilizing a linear regression method. Excess Green (ExG) vegetation indices for each of the test plots were compared with the M-statistic method in order to quantitatively evaluate the greenness of soybean fields under different cropping systems. Results: The reflectance calibration methods used in the study showed high coefficients of determination, ranging from 0.8 to 0.9, indicating the feasibility of a linear regression fitting method for monitoring multi-temporal RGB images of soybean fields. As expected, the ExG vegetation indices changed according to different soybean growth stages, showing clear differences among the test plots with different cropping treatments in the early season of 1. Conclusion: Therefore, multi-temporal images obtained with an UAV and a RGB camera could be applied for quantifying overall vegetation fractions and crop growth status, and this information could contribute to determine proper treatments for the vegetation fraction.
The aim of the project was to determine if including a-priori information in the system identification multisine maneuver design process will improve the quality of the estimated data in the lateral motion. During this study we used a linear aircraft model which was containing a-priori information in terms of aerodynamic derivatives. A-priori knowledge allowed us to obtain a non-uniform power spectrum of the signals that excited the ailerons and rudder.
Three cases were analyzed for comparison. The first design was obtained when no a-priori information was available. The second case was designed on the basis of the a-priori model state matrix. We found dominant frequencies of the system and assigned to them more power. In the third case the non-uniform power spectrum was optimized. A genetic algorithm was used for that purpose. The efficiency of the design was optimized thought out Relative Peak Factor. The a-priori information was introduced by using Marchand methodology.
All the obtained maneuvers were used to excite the aircraft model. The response of the system was registered. Recorded data was used later to perform a parameter estimation for each case. In the end we compared the results from the identification process in terms of the accuracy.
Micro Unmanned Aerial Vehicles (UAV) are used in a variety of agricultural research applications. However, UAVs are often not designed to interact with their attached sensor devices. This circumstance leads to disadvantages in sensor data acquisition and leads to increased efforts in post-mission data processing. This paper introduces a real-time sensor data software framework, executed on an embedded low-cost computer to aggregate carrier and sensor device measurements. The proposed setup provides functionality to control sensor devices and to retrieve, fuse, log and broadcast sensor data on-the-fly. A prototype implementation proved its functionality in an example use case of above-field micro-climate mapping with low-cost temperature, humidity and image sensors.
Precision Agriculture (PA) is an approach to managing the variability in production agriculture in a more economic and environmentally efficient manner. It has been pioneered as a management tool in the grains industry, and while its development and uptake continues to grow amongst grain farmers worldwide, a broad range of other cropping industries have embraced the concept This book explains general PA theory, identifies and describes essential tools and techniques, and includes practical examples from the grains industry. Readers will gain an understanding of the magnitude, spatial scale and seasonality of measurable variability in soil attributes, plant growth and environmental conditions. They will be introduced to the role of sensing systems in measuring crop, soil and environment variability, and discover how this variability may have a significant impact on crop production systems. Precision Agriculture for Grain Production Systems will empower crop and soil science students, agronomy and agricultural engineering students, as well as agronomic advisors and farmers to critically analyse the impact of observed variation in resources on crop production and management decisions.
8 Chapters, 199 pages
Introduction to Precision Agriculture
Global Navigation Satellite Systems and Precision Agriculture
Hardware for Precision Agriculture
Software for Precision Agriculture
Data management for Precision Agriculture
Making and interpreting maps for Precision Agriculture
Yield variability and site-specific crop management
Economics of Precision Agriculture
- Robert P.