Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

Simulation and Test of Discrete Mobile Surfaces for a RC-Aircraft

Morphing structures suitable for unmanned aerial vehicles (UAVs) have been investigated for several years. This paper presents a novel lightweight, morphing concept based on the exploitation of the “lever effect” of a bistable composite plate that can be integrated in an UAV horizontal tail. Flight dynamics equations are solved in Simulink environment, thus being able to simulate and compare different flight conditions with conventional and bistable command surfaces. Subsequently, bistable plates are built by using composite materials, paying particular attention to dimensions, asymmetric stacking sequence and total thickness needed to achieve bistability. NACA0011 airfoil is chosen for proving this concept. Wind tunnel tests demonstrate that the discrete surface is capable of withstanding the aerodynamic pressure. A remotely piloted vehicle is employed to test the discrete horizontal tail command during the take-off. The results show that, choosing a proper configuration of constraints, stacking sequence and aspect ratio for the bistable laminate, it is possible to tailor the snap-through mechanism. The proposed concept appears lighter and increases aerodynamic efficiency when compared to conventional UAV command surfaces.

Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Vegetation is commonly monitored to improve efficiency of various agricultural practices. Spatial and temporal changes in plant growth and development can be monitored with the aid of remote sensing techniques employing ground, aerial, and satellite platforms. Unmanned aerial vehicles (UAV) and multi-spectral cameras developed for UAVs have an important potential for agricultural management activities with high-resolution spatial and temporal images. However, UAV images should be assessed based on ground measurements for using these images as a decision-support tool in agriculture. This study was conducted to estimate sunflower leaf area index (LAI) and yield with the aid of Normalized Difference Vegetation Index (NDVI) images generated from raw UAV images. Furthermore, UAV-based NDVI values were compared with NDVI values calculated by using hyper-spectral measurements carried out with a ground-based spectroradiometer. Between July and August of 2017, six flight missions were conducted and spectral measurements were made simultaneously. A significant correlation (R² = 0.77) was determined between NDVI values that belong to UAV platform and spectroradiometer. Also, regression models developed for sunflower LAI and yield estimation depending UAV-based NDVI have R² values of 0.88 and 0.91, respectively.