Precision Landing Test and Simulation of the Agricultural UAV on Apron

Guo, Yangyang; Guo, Jiaqian; Liu, Chang; Xiong, Hongting; Chai, Lilong; He, Dongjian

doi:10.3390/s20123369

Cited by 19 publications

(13 citation statements)

References 21 publications

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“…In addition, the advanced RS techniques such as unmanned aerial vehicle remote sensing (UAV-RS) can be applied to acquire filed observation data at a fine spatial resolution (centimeter-level). Unlike the traditional satellite remote sensing (SRS) that is commonly limited by the spatial and spectral resolutions, and long revisit cycle, the UAV-RS can provide images at adequate spatial and temporal resolutions without the limitations from the weather condition [ 23 , 24 , 25 ]. It is also important for agricultural and ecological applications as they can be easily deployed and possessed the ability to dynamically monitor the crops in detail during important phenology events such as flowering, heading, and mature that are the critical growth stages of crops.…”

Section: Introductionmentioning

confidence: 99%

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods

Guo

Yin

Sun

et al. 2020

Sensors

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Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red–green–blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture.

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Section: Introductionmentioning

confidence: 99%

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods

Guo

Yin

Sun

et al. 2020

Sensors

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“…The quadcopter, with its vertical take-off and landing capability, simple mechanical structure, and easy maintenance, has been widely used in a variety of military and civilian mission scenarios, such as search and rescue [ 1 , 2 ], package delivery [ 3 ], border patrol [ 4 ], military surveillance [ 5 ], and agricultural applications [ 6 , 7 ]. Abnormal and unexpected situations, such as actuator failure, sensor failure, and structural failure could occur during the flight of the quadcopter.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Zhang

Zhao

Wang

et al. 2021

Sensors

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Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.

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“…The authors concluded that the system setup of a mini-UAV sensory system could be used for obtaining environmental variables. Another study that could be found of UAV in agricultural indoor environment is the work of [21] which covers a precision landing control approach for agricultural UAV. By using GPS for localization and image recognition for localization of the landing platform, the authors made a basis setup for landing control in animal houses.…”

Section: Introductionmentioning

confidence: 99%

Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

Krul

Pantos

Frangulea

et al. 2021

Drones

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Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology.

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Precision Landing Test and Simulation of the Agricultural UAV on Apron

Cited by 19 publications

References 21 publications

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

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