Browsing by Author "Pieterse, Johannes Lodewyk"
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- ItemA predictive model for precision tree measurements using applied machine learning(Stellenbosch : Stellenbosch University, 2022-04) Pieterse, Johannes Lodewyk; Nel, Stephan; Drew, David M.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.ENGLISH ABSTRACT: Accurately determining biological asset values is of great importance for forestry enterprises — the process ought to be characterised by the proper collection of tree data by means of utilising appropriate enumeration practices conducted at managed forest compartments. Currently, only between 5–20% of forest areas are enumerated which serve as a representative sample for the entire enclosing compartment. For forestry companies, timber volume estimations and future growth projections are based on these statistics, which may be accompanied by numerous unintentional errors during the data collection process. Many alternative methods towards estimating and inferring tree data accurately are available in the literature — the most popular characteristic is the so-called diameter at breast height (DBH), which can also be measured by means of remote sensing techniques. The advancements in laser scanning measurement apparatuses are significant in recent decades, however, these approaches are notably expensive and require specialised and technical skills for their operation. One of the main drawbacks associated with the measurement of DBH by means of laser scanning is the lack of scalability — equipment setup and data capture are arduous processes that take a significant amount of time to complete. Algorithmic breakthroughs in the domain of data science, predominantly spanning machine learning (ML) and deep learning (DL) approaches, warrant the selection and practical application of computer vision (CV) procedures. More specifically, an algorithmic approach towards monocular depth estimation (MDE) techniques was employed for the extraction of tree data features from video recordings (captured using no more than an ordinary smartphone device) and are investigated in this thesis. Towards this end, a suitable forest study area was identified to conduct the experiment and the industry partner of the project, i.e. the South African Forestry Company SOC Limited (SAFCOL) granted the necessary plantation access. The research methodology adopted for this thesis includes fieldwork at the given site, which involved first performing data collection steps according to accepted and standardised operating procedures developed for tree enumerations. This data set is regarded as the “ground truth” and comprises the target feature (i.e. actual DBH measurements) later used for modelling purposes. The video files were processed in a structured manner in order to extract tree segment patterns from the correspond ing imagery. Various ML models are then trained and tested in respect of the basic input feature data file, which produced a relative root mean squared error (RMSE %) between 14.1 and 18.3% for the study. The relative bias yields a score between −0.08% and 1.13% indicating that the proposed workflow solution exhibits a consis tent prediction result, but at an undesirable error rate (i.e. RMSE) deviation from the target output. Additionally, the suggested CV/ML workflow model is capable of generating a dis cernibly similar spatial representation upon visual inspection (when compared with the ground truth data set — i.e. tree coordinates captured during fieldwork). In the pursuit of precision forestry, the proposed predictive model developed for accurate tree measurements produce DBH estimations that approximate real-world values with a fair degree of accuracy.