College of Engineering

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2D and 3D Mapping of a Littoral Zone with UAS and Structure from Motion Photogrammetry
(2015-05) Giessel, Justin Zachary
Advancements in the miniaturization of sensors and their integration in light‐weight, smallscale unmanned aerial systems (UAS) have resulted in an explosion of uses for inexpensive and easily obtained remotely sensed data. This study examines the capabilities of a small‐scale UAS equipped with a consumer grade RGB camera for 2D and 3D mapping of a sandy bay shoreline using Structure from Motion (SfM) photogrammetry. Several key components are analyzed in order to assess the utility of UAS‐based SfM photogrammetry for beach and boundary surveying of the littoral zone. First, the accuracy of the 3D point cloud produced by the SfM densification process over the beach is compared to high accuracy RTK GPS transects. Results show a mean agreement of approximately 7.9 cm over the sub‐aerial beach with increased error in shallow water. Minimal effects of beach slope on vertical accuracy were observed. Secondly, bathymetric measurements extracted from the UAS/SfM point cloud are examined, and an optical inversion approach is implemented where the SfM method fails. Results show that a hybrid elevation model of the beach and littoral zone consisting of automatic SfM products, post‐processed SfM products, and optical inversion provide the most accurate results when mapping over turbid water. Finally, SfM‐derived shoreline elevation contour (boundary) is compared to a shoreline elevation contour derived using the currently accepted RTK GPS method for conducting legal littoral boundary surveys in the state of Texas. Results show mean planimetric offsets < 25 cm demonstrating the potential of UAS‐based SfM photogrammetry for conducting littoral boundary surveys along non‐occluded, sandy shorelines.
3-D Hybrid Trajectory Modeling for Unmanned Aerial Vehicles (UAVS)
(2019-08) Wang, Baoqian; Xie, Junfei; Garcia Carrillo, Luis Rodolfo; Zhang, Ning
The burgeoning use of unmanned aerial vehicles (UAVs) evidences forthcoming environments where innumerable UAVs will appear in the National Airspace System (NAS). The UAS traffic man- agement (UTM) aims to provide solutions to enable safe integration of numerous UAVs into the NAS, but the design of effective UTM strategies faces significant challenges. One of the challenges is to develop high-fidelity trajectory models for UAVs of partially known or unknown dynamics. Tradi- tional physics-based models that require costly system identifications and field tests, and data-based models that require large amount of real flight data may not be feasible. To address this challenge, this paper introduces a hybrid 3-dimensional (3-D) UAV trajectory modeling framework, which in- tegrates the physics-based and data-based models to capture the dynamics of UAVs of interest with high accuracy using only a small amount of real flight data. Simulation studies and field tests validate and demonstrate the good performance of the proposed framework.
3D Characterization of sorghum panicles using a 3D point cloud derived from UAV imagery
(MDPI, 2021-01-15) Chang, Anjin; Jung, Jinha; Yeom, Junho; Landivar, Juan
Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed for assessing genetic diversity, variety selection, and yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed a detecting and characterizing method for individual sorghum panicles using a 3D point cloud derived from UAV images. The RGB color ratio was used to filter non-panicle points out and select potential panicle points. Individual sorghum panicles were detected using the concept of tree identification. Panicle length and width were determined from potential panicle points. We proposed cylinder fitting and disk stacking to estimate individual panicle volumes, which are directly related to yield. The results showed that the correlation coefficient of the average panicle length and width between the UAV-based and ground measurements were 0.61 and 0.83, respectively. The UAV-derived panicle length and diameter were more highly correlated with the panicle weight than ground measurements. The cylinder fitting and disk stacking yielded R2 values of 0.77 and 0.67 with the actual panicle weight, respectively. The experimental results showed that the 3D point cloud derived from UAV imagery can provide reliable and consistent individual sorghum panicle parameters, which were highly correlated with ground measurements of panicle weight.
Adaptive neural network-based event-triggered control of single-input single-output nonlinear discrete-time systems
(IEEE, 2015-10-26) Sahoo, Avimanyu; Xu, Hao; Sarangapani, Jagannathan
This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.
Age-optimal mobile elements scheduling for recharging and data collection in green IoT
(IEEE, 2020-04-28) Ma, Jianxin; Shi, Shuo; Gu, Shushi; Zhang, Ning; Xuemai, Gu
Ensuring real-time reporting of fresh information and maintaining the sustainability of power supply is of great importance in time-critical green Internet of Things (IoT). In this paper, we investigate the mobile element scheduling problem in a network with multiple independent and rechargeable sensors, in which mobile elements are dispatched to collect data packets from the sensor nodes and to recharge them. The age of information (AoI) is used to measure the time elapsed of the most recently delivered packet since the generation of the packet. We propose an age-optimal mobile elements scheduling (AMES), which decides the trajectories of mobile elements based on a cooperative enforcement game and completes the time-slot allocation in each meeting point, to minimize the average AoI and maximize the energy efficiency. The cooperative enforcement game enables the mobile elements to make optimal visiting decisions and avoid the visiting conflicts, and the outcome of the game is pareto-optimal. Compared to the existing approaches, i.e., greedy algorithm (GA), greedy-neighborhood algorithm (GA-neighborhood), simulation results demonstrate that AMES can achieve a lower average AoI and a higher energy efficiency with a higher successful visiting ratio of the sensor node.
Aggregating XAI methods for insights into geoscience models with correlated and high-dimensional rasters
(2023-05-13) Krell, Evan; Kamangir, Hamid; Collins, Waylon; King, Scott A; Tissot, Philippe
Geoscience applications have been using sophisticated machine learning methods to model complex phenomena. These models are described as black boxes since it is unclear what relationships are learned. Models may exploit spurious associations that exist in the data. The lack of transparency may limit user’s trust, causing them to avoid high performance models since they cannot verify that it has learned realistic strategies. EXplainable Artificial Intelligence (XAI) is a developing research area for investigating how models make their decisions. However, XAI methods are sensitive to feature correlations. This makes XAI challenging for high-dimensional models whose input rasters may have extensive spatial-temporal autocorrelation. Since many geospatial applications rely on complex models for target performance, a recommendation is to combine raster elements into semantically meaningful feature groups. However, it is challenging to determine how best to combine raster elements. Here, we explore the explanation sensitivity to grouping scheme. Experiments are performed on FogNet, a complex deep learning model that uses 3D Convolutional Neural Networks (CNN) for coastal fog prediction. We demonstrate that explanations can be combined with domain knowledge to generate hypotheses about the model. Meteorological analysis of the XAI output reveal FogNet’s use of channels that capture relationships related to fog development, contributing to good overall model performance. However, analyses also reveal several deficiencies, including the reliance on channels and channel spatial patterns that correlate to the predominate fog type in the dataset, to make predictions of all fog types. Strategies to improve FogNet performance and trustworthiness are presented.
AI radar sensor: Creating radar depth sounder images based on generative adversarial network
(MDPI, 2019-12-12) Rahnemoonfar, Maryam; Johnson, Jimmy; Paden, John
Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.
Air-ground integrated mobile edge networks: A survey
(IEEE, 2020-07-09) Zhang, Wen; Li, Longzhuang; Zhang, Ning; Tao, Han; Wang, Shangguang
With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.
Analysis of microalgal density estimation by using LASSO and image texture features
(2023-02-24) Nguyen, Linh; Nguyen, Dung Kim; Nguyen, Thang; Nguyen, Thanh Binh; Nghiem, Truong
Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively.
Applications of deep learning and multi-perspective 2D/3D imaging streams for remote terrain characterization of coastal environments
(2021-12) Pashaei, Mohammad; Starek, Michael J.; Tissot, Philippe; King, Scott A.; Glennie, Craig L.; Lynch-Davis, Kathleen
Threats from storms, sea encroachment, and growing population demands put coastal communities at the forefront of engineering and scientific efforts to reduce vulnerabilities for their long-term prosperity. Updated and accurate geospatial information about land cover and elevation (topography) is necessary to monitor and assess the vulnerability of natural and built infrastructure within coastal zones. Advancements in remote sensing (RS) and autonomous systems extend surveying and sensing capabilities to difficult environments, enabling more geospatial data acquisition flexibility, higher spatial resolutions, and allowing humans to “see” in ways previously unattainable. Recent years have witnessed enormous growth in the application of small, unmanned aircraft systems (UASs) equipped with digital cameras for hyperspatial resolution imaging and dense three-dimensional (3D) mapping using structure-from-motion (SfM) photogrammetry techniques. In contrast to photogrammetry, light detection and ranging (lidar) is an active RS technique that uses a pulsed laser mounted on a static or mobile platform (from air or land) to scan in high definition the 3D structure of a scene. Rapid proliferation in lidar technology has resulted in new scanning and imaging modalities with ever increasing capabilities such as geodetic-grade terrestrial laser scanning (TLS) with ranging distances of up to several kilometers from a static tripod. TLS enables 3D sampling of the vertical structure of occluding objects, such as vegetation, and underlying topography. Full waveform (FW) lidar systems have led to a significant increase in the level of information extracted from a backscattered laser signal returned from a scattering object. With this technological advance and increase in remote sensing capabilities and data resolution, comes an increase in information gain at the cost of highly more complex and challenging big data sets to process and extract meaningful information. In this regard, utilizing end-to-end analyzing techniques recently developed in artificial intelligence (AI), in particular, convolutional neural network (CNN), developed under deep learning (DL) framework, seems applicable. DL techniques have recently outperformed state-of-the-art analysis techniques in a wide range of applications including RS. This work presents the application of DL for efficient exploitation of hyperspatial UAS-SfM photogrammetry and FW TLS data for land cover monitoring and topographic mapping in a coastal zone. Hyperspatial UAS images and TLS point cloud data with additional information about the scattering properties of illuminated target in the footprint of the laser beam encoded in returned waveform signals provide valuable geospatial data resources to uncover the accurate 3D structure of the surveyed environment. This study presents three main contributions: 1) Evaluation of different DCNN architectures, and their efficiencies, to classify land cover within a complex wetland setting using UAS imagery is investigated; 2) DCNN-based single image super-resolution (SISR) is employed as a pre-processing technique on low-resolution UAS images to predict higher resolution images over coastal terrain with natural and built land cover, and its effectiveness for enhancing dense 3D scene reconstruction with SfM photogrammetry is tested; 3) Full waveform TLS data is employed for point cloud classification and ground surface detection in vegetation using a developed DCNN framework that works directly off of the raw, digitized echo waveforms. Results show that returned raw waveform signals carry more information about a target’s spatial and radiometric properties in the footprint of the laser beam compared to waveform attributes derived from traditional waveform processing techniques. Collectively, this study demonstrates useful information retrieval from hyperspatial resolution 2D/3D RS data streams in a DL analysis framework.
Approximate optimal control of affine nonlinear continuous-time systems using event-sampled neurodynamic programming
(IEEE, 2016-04-07) Sahoo, Avimanyu; Xu, Hao; Sarangapani, Jagannathan
This paper presents an approximate optimal control of nonlinear continuous-time systems in affine form by using the adaptive dynamic programming (ADP) with event-sampled state and input vectors. The knowledge of the system dynamics is relaxed by using a neural network (NN) identifier with event-sampled inputs. The value function, which becomes an approximate solution to the Hamilton–Jacobi–Bellman equation, is generated by using event-sampled NN approximator. Subsequently, the NN identifier and the approximated value function are utilized to obtain the optimal control policy. Both the identifier and value function approximator weights are tuned only at the event-sampled instants leading to an aperiodic update scheme. A novel adaptive event sampling condition is designed to determine the sampling instants, such that the approximation accuracy and the stability are maintained. A positive lower bound on the minimum inter-sample time is guaranteed to avoid accumulation point, and the dependence of inter-sample time upon the NN weight estimates is analyzed. A local ultimate boundedness of the resulting nonlinear impulsive dynamical closed-loop system is shown. Finally, a numerical example is utilized to evaluate the performance of the near-optimal design. The net result is the design of an event-sampled ADP-based controller for nonlinear continuous-time systems.
Assessing land leveling needs and performance with unmanned aerial system
(SPIE, 2018-01-02) Enciso, Juan; Jung, Jinha; Chang, Anjin; Chavez, Jose Carlos; Yeom, Junho; Landivar, Juan; Cavazos, Gabriel
Land leveling is the initial step for increasing irrigation efficiencies in surface irrigation systems. The objective of this paper was to evaluate potential utilization of an unmanned aerial system (UAS) equipped with a digital camera to map ground elevations of a grower’s field and compare them with field measurements. A secondary objective was to use UAS data to obtain a digital terrain model before and after land leveling. UAS data were used to generate orthomosaic images and three-dimensional (3-D) point cloud data by applying the structure for motion algorithm to the images. Ground control points (GCPs) were established around the study area, and they were surveyed using a survey grade dual-frequency GPS unit for accurate georeferencing of the geospatial data products. A digital surface model (DSM) was then generated from the 3-D point cloud data before and after laser leveling to determine the topography before and after the leveling. The UAS-derived DSM was compared with terrain elevation measurements acquired from land surveying equipment for validation. Although 0.3% error or root mean square error of 0.11 m was observed between UAS derived and ground measured ground elevation data, the results indicated that UAS could be an efficient method for determining terrain elevation with an acceptable accuracy when there are no plants on the ground, and it can be used to assess the performance of a land leveling project.
Assessing lodging severity over an experimental maize (Zea Mays L.) Field using UAS images
(MDPI, 2017-09-04) Chu, Tianxing; Starek, Michael J.; Brewer, Michael J.; Murray, Seth C.; Pruter, Luke S.
Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.
Assessing the effect of drought on winter wheat growth using unmanned aerial system (UAS) -based phenotyping
(MDPI, 2021-03-17) Bhandari, Mahendra; Baker, Shannon; Rudd, Jackie C.; Ibrahim, Amir M. H.; Chang, Anjin; Xue, Qingwu; Jung, Jinha; Landivar, Juan; Auvermann, Brent
Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.
Assessing the effect of drought on winter wheat growth using unmanned aerial system (UAS)-based phenotyping
(MDPI, 2021-03-17) Bhandari, Mahendra; Baker, Shannon; Rudd, Jackie C.; Ibrahim, Amir M. H.; Chang, Anjin; Xue, Qingwu; Jung, Jinha; Landivar, Juan; Auvermann, Brent
Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.
Assessing the effects of irrigation water salinity on two ornamental crops by remote spectral imaging
(MDPI, 2/20/2021) Yu, Xinyang; Her, Younggu; Chang, Anjin; Song, Jung-Hun; Campoverde, E. Vanessa; Schaffer, Bruce
Salinity is one of the most common and critical environmental factors that limit plant growth and reduce crop yield. The aquifers, the primary sources of irrigation water, of south Florida are shallow and highly permeable, which makes agriculture vulnerable to projected sea level rise and saltwater intrusion. This study evaluated the growth responses of two ornamental nursery crops to the different salinity levels of irrigation water to help develop saltwater intrusion mitigation plans for the improved sustainability of the horticultural industry in south Florida. Two nursery crops, Hibiscus rosa-sinensis and Mandevilla splendens, were treated with irrigation water that had seven different salinity levels from 0.5 (control) to 10.0 dS/m in the experiment. Crop height was measured weekly, and growth was monitored daily using the normalized difference vegetation index (NDVI) values derived from multispectral images collected using affordable sensors. The results show that the growth of H. rosa-sinensis and M. splendens was significantly inhibited when the salinity concentrations of irrigation water increased to 7.0 and 4.0 dS/m, for each crop, respectively. No significant differences were found between the NDVI values and plant growth variables of both H. rosa-sinensis and M. splendens treated with the different irrigation water salinity levels less than 2.0 dS/m. This study identified the salinity levels that could reduce the growth of the two nursery crops and demonstrated that the current level of irrigation water salinity (0.5 dS/m) would not have significant adverse effects on the growth of these crops in south Florida.
Automated open cotton boll detection for yield estimation using unmanned aircraft vehicle (UAV) data
(MDPI, 2018-11-27) Yeom, Junho; Jung, Jinha; Chang, Anjin; Maeda, Murilo; Landivar, Juan
Unmanned aerial vehicle (UAV) images have great potential for various agricultural applications. In particular, UAV systems facilitate timely and precise data collection in agriculture fields at high spatial and temporal resolutions. In this study, we propose an automatic open cotton boll detection algorithm using ultra-fine spatial resolution UAV images. Seed points for a region growing algorithm were generated hierarchically with a random base for computation efficiency. Cotton boll candidates were determined based on the spatial features of each region growing segment. Spectral threshold values that automatically separate cotton bolls from other non-target objects were derived based on input images for adaptive application. Finally, a binary cotton boll classification was performed using the derived threshold values and other morphological filters to reduce noise from the results. The open cotton boll classification results were validated using reference data and the results showed an accuracy higher than 88% in various evaluation measures. Moreover, the UAV-extracted cotton boll area and actual crop yield had a strong positive correlation (0.8). The proposed method leverages UAV characteristics such as high spatial resolution and accessibility by applying automatic and unsupervised procedures using images from a single date. Additionally, this study verified the extraction of target regions of interest from UAV images for direct yield estimation. Cotton yield estimation models had R2 values between 0.63 and 0.65 and RMSE values between 0.47 kg and 0.66 kg per plot grid.
Automated radar heading calibration with collaborating participants and multi-sensor fusion
(2021-12) Boyd, Josh; King, Scott A.; Li, Longzhuang; Wang, Wenlu
As unmanned aerial systems (UAS) become more prolific so will the use of radar systems for tracking UAS in the national airspace system (NAS). The future of Urban Air Mobility (UAM) involves large amounts of UAS operating autonomously and simultaneously in urban environments for the purpose of passenger or cargo transportation. Radar detection of UAS in an urban environment can be hindered by line of sight (LOS) blockage by large buildings thus necessitating many surveillance devices to gain full coverage. Currently, UAM is still in development by the Federal Aviation Administration (FAA) and other airspace partners and many cities do not have the need or resources for full radar coverage. Due to the high cost of individual radar systems and the quantity needed to cover urban areas it is currently not practical to have full radar coverage of an area at all times. Permanent stationary radar systems are generally calibrated once with occasional adjustments and low time constraints. Temporary radar systems must be calibrated and aligned before each mission deployment and often under short time constraints. Temporarily stationed mobile radar platforms will be utilized for specific targeted mission objectives until a more permanent solution is developed and implemented. In the case of disaster response or search and rescue, a temporary radar system needs to be quickly deployed. The key abilities required by a temporary radar system are accurate track position reporting and quick setup and breakdown. One of the bottlenecks to quick setup is heading calibration. Radar antenna alignment is crucial to the performance of the system and its ability to accurately determine the position of a tracked object. In this paper, we implement and compare multiple methods of radar heading calibration for accuracy and speed including manually with a handheld compass, manually with a web based heading helper tool, manually with a custom dual Real Time Kinetic (RTK) GPS alignment tool, and automated with a collaborating Radar Cross Section (RCS) device. For RCS devices we use a marine radar reflector and attached RTK GPS when unable to fly and an unmanned aerial vehicle (UAV) also with RTK GPS when able to fly. By leveraging our experience working with UAVs and Radars we show a method to autocalibrate the positioning sensors by using multisensory fusion and collaborating participants, thus reducing the amount of setup time, and increasing the accuracy of the system.
Automated system for evaluating consistency between CAD model and 3D scan of vehicle seat
(Springer, 2022-02-02) Park, Byoung-Keon D.; Park, Jangwoon; Lee, Byung Cheol; Lee, Baekhee
Reducing the number of design changes in vehicle seat development is critical for minimizing both production cost and product lead time. Generally, discrepancies in measured dimensional specifications of vehicle-seat prototypes and computer-aided design (CAD) models cause significant quality control issues of the finished products. Although three-dimensional (3D) scanning technology enables the efficient evaluations and inspection processes of vehicle-seat prototypes, many evaluation processes require time-consuming tasks. This paper proposes an automated system for evaluating a geometrical consistency between a 3D scan of a prototype and the original CAD model. In the current study, the existing evaluation processes conducted by seat engineers were examined by survey questionnaires. The survey responses were analyzed to define a standardized evaluation process for the automated system. Various computational algorithms, including a function-based scan-to-CAD registration, standard seat dimension estimation, and template-based reporting algorithms, were developed to evaluate the scan and CAD consistency automatically. The developed system not only reduced over 99 % of the evaluation time (on average, existing method: > 2 hrs per seat and system method: < 5 min per seat) but also increased the repeatability of evaluations. Furthermore, the system can collect dimensions of diverse seat designs, prototypes, and products to construct a database of seat dimensions for benchmarking and design improvement.
Automatic canopy plot boundary detection using computer vision
(2020-05) Wynn, De Kwaan; King, Scott A.; Gonzales, Xavier; Li, Longzhuang
In Corpus Christi, Texas the United States Department of Agricultre (USDA) funded Texas A&M Agrilife research on large farmlands with hundreds of individual cotton vegetation plots. Each plot is planted uniformly in rows but not all plots grow at the same rate. Every week the plots are photographed using an Unmanned Aircraft System (UAS) flying at a height of 100 feet to record and evaluate growth for various reasons. The research scientists and farmer’s current method of localizing individual plots of vegetation within an image has proven to be very time consuming and inefficient. The algorithm developed in this paper automates the localization process under sunny conditions. The algorithm uses the Hue, Saturation, and Value (HSV) color space during the preprocessing stage to provide a binary image that indicates where each green pixel is located. Various OpenCV functions are then used to automate the crop localization process. Minimum and Maximum threshold values are set for filtering by size sections of the algorithm. Then, morphological operations are employed to further refine the regions of interest. The Connected Components function is used to determine how large each remaining object is and that size is then used to determine how large each localizing polygon will be drawn. After the size of each object is found, the size of each localizing polygon to be drawn is evaluated and split into smaller polygons whenever necessary. Not only the developed algorithm able to detect and classify cotton crop locations quickly but it is able to handle various complex situations. The developed method was evaluated by its accuracy, precision, and recall, which were 92.4%, 100%, and 92.4% respectively.

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