TAMU-CC Theses, Dissertations, and Other Projects
Permanent URI for this communityhttps://hdl.handle.net/1969.6/1
Find theses, dissertations, and other projects completed by students of Texas A&M University-Corpus Christi. Associated files for theses, dissertations, and other projects, such as data sets and Honors Projects of Excellence, can also be found within this community.
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Browsing TAMU-CC Theses, Dissertations, and Other Projects by Department "Geospatial Surveying Engineering"
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Item Application of UAS photogrammetry and geospatial AI techniques for palm tree detection and mapping(2023-08) Regmi, Pratikshya; Starek, Michael; Chu, Tianxing; Medrano, AntonioUncrewed aircraft systems (UAS), commonly known as drones, underwent significant advance ments in recent years, particularly in the development of improved sensors and cameras that enabled high-resolution imagery and precise measurements. This study utilized a UAS to capture aerial imagery of Texas A & M University-Corpus Christi (TAMUCC) main campus, which was then processed using Structure-from-Motion (SfM) photogrammetric software to generate orthomosaic imagery. The primary purpose of this study was to utilize the orthomosaic imagery acquired from UAS to detect, map, and quantify the number of palm trees. Initially, three deep-learning models were trained using the same set of training samples. The model exhibiting the highest performance in terms of precision, recall, and F1-Score was selected as the optimal model. The model obtained through the fine-tuning of a pre-trained GIS-based model with additional training samples was identified as the optimal choice, yielding the following values: precision=0.88, recall=0.95, and F1-score=0.91. This model successfully detected a total of 1414 sabal palm trees within our study area. The chosen optimal model was employed to examine the impact of ground sampling distance (GSD) on the deep learning model. GSD values were varied, namely 5 cm, 10 cm, 20 cm, and 40 cm. The findings revealed that the model’s performance deteriorated as the resolution decreased. Furthermore, the optimal model was subjected to an additional test using multi-temporal datasets with approximately the same GSD (1.5 cm). These datasets included one acquired a year prior to the model’s training datasets, and another obtained three months after the training datasets. Remarkably, the results demonstrated that the model maintained a comparable level of accuracy across all three testing datasets. The obtained results were verified using ground truth values taken in a small portion of the study area. This study concludes that deep learning models for object detection exhibit superior performance when fine-tuned with training samples specific to the area of interest. Furthermore, it is evident that the optimal model’s effectiveness diminishes significantly when the imagery resolution is reduced. Additionally, the performance of the deep learning model remains relatively consistent when applied to datasets acquired at different time frames, as long as the resolution of the testing data remains the same. In summary, the application of deep learning demonstrates its efficacy, user-friendliness, and time-saving capabilities for object detection. This study shows how we can use UAS and deep learning to detect palm trees. It helps us develop better ways to monitor and manage palm trees.Item Autonomous harvesting via hierarchical reinforcement learning in dynamic environments(2023-05) Nethala, Prasad; Huang, Yuxia (Lucy); Dugan, Um; Starek, MichaelSmart farming not only requires geospatial navigation but also uses various microprocessors and sensors to perform functions such as controlling temperature and irrigation systems. Advanced phenotyping modalities such as IoT and digital twin technologies revamped agriculture productivity to an extent hitherto unprecedented. However, matching crop cultivation and harvesting technology has yet to be further advanced to take advantage of data-driven crop production. Farming areas are often unstructured with dynamic objects such as human workers and farming machines. Therefore, a smart harvesting robot is in need of automatic navigation and obstacle avoidance. Due to conflicting objectives of goal-reaching and obstacle-avoidance, especially in a dynamic environment, harvesting is a challenging task for a robotic system. In this thesis, a novel Hierarchical Reinforcement Learning architecture is proposed, which is a robust multitask-capable AI model for an autonomous mobile manipulator to achieve both terrain coverage while assuring obstacle avoidance with dynamic objects. It is assumed that the manipulator is equipped with sensitive skin for omnidirectional sensitivity. The proposed Hierarchical Reinforcement Learning architecture is modeled with both Deep Deterministic Policy Gradient (DDPG) algorithm and Proximal Policy Optimization (PPO) algorithm. As a result, two different hierarchical architectures are developed as Hierarchical Deep Deterministic Policy Gradient (HDDPG) and Hierarchical Proximal Policy Optimization (HPPO) algorithms to autonomously manage two separate agents for both goal-reaching and obstacle-avoidance objectives. Transfer learning is adopted to assess if the trained models were overfit or underfit as well as for learning generalized policy. The algorithms were evaluated in a simulated environment by collecting fallen fruits in a crowded orchard farm environment with a variety of dynamic obstacles after being taught in a simple environment with fewer constraints. The metric used for this mission includes percent harvesting and the number of goal touch, the number of obstacle touch, navigation distance, and navigation time. HDDPG outperformed the remaining algorithms by 70% in terms of total average rewards and minimum pixel distance travel, whereas HPPO achieved the highest number of fruit collections, DDPG and PPO were unable to complete the test environment due to local minimum. Both Hierarchical architectures HDDPG and HPPO could successfully generalize to new situations beyond the training environments with robust performance.Item A comparative analysis of georeferencing techniques for crop canopy height estimation using UAS photogrammetry(2023-08) Landivar Scott, Jose Luis; Starek, Michael; Bhandari, Mahendra; Chu, TianxingIn the rapidly evolving fields of geospatial engineering and precision agriculture, the accuracy and reliability of georeferencing techniques and Uncrewed Aircraft System (UAS) methodologies are crucial for effective decision-making and crop management. This research aims to enhance UAS Structure-from-Motion (SfM) photogrammetry data quality for crop canopy height estimation in high-throughput phenotyping. The study investigates and compares the accuracy and reliability of three distinct methods used for georeferencing of the UAS imagery, which subsequently enables more accurate SfM 3D reconstruction: Global Navigation Satellite System (GNSS) without any correction aiding (GNSS-only), GNSS+Real-Time Kinematic (RTK), receiving RTK corrections from a local base station, GNSS+Real-Time Network (RTN), receiving RTK corrections from the Texas Department of Transportation (TxDOT) GNSS reference station network. The study further assesses the correlation between manually measured plant heights and those estimated from UAS-SfM point cloud data, exploring three different Digital Terrain Model (DTM) generation techniques. The research was conducted at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, USA, on corn crops grown during the 2022 agricultural season. The three DTM generation methods under consideration included 1) using a DTM acquired from a flight conducted before plant emergence, 2) creating a DTM by interpolating ground height points, and 3) implementing automatic classification algorithms. Findings initially revealed that the GNSS+RTK method consistently outperformed the other georeferencing techniques, delivering more accurate results across various dates. Despite these overall trends, there were some instances where the GNSS+RTK method did not consistently outperform the other techniques. The use of one ground control point (GCP) improved georeferencing accuracy compared to scenarios with no GCPs used, while GNSS-only without correction aiding reported the least accurate results as expected. Regarding plant height estimation, the highest accuracy was generally achieved with greater canopy cover percentages, with the optimal percentage varying depending on the data collection date and DTM creation method. The highest coefficient of determination (R2) of 0.92 between manual measurements and UAS-SfM derived plant heights was found when the DTM was either interpolated from ground height points or obtained from a pre-emergence flight.