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Turfgrass management represents a critical aspect of landscaping, sports fields, and golf course maintenance. The efficient logistics of turfgrass, encompassing tasks such as mowing, fertilization, and irrigation, can significantly impact its health and aesthetics. In recent years, remote sensing technologies and machine learning have emerged as powerful tools for optimizing turfgrass logistics. This work presents a comprehensive study on the application of machine learning algorithms to enhance the management of turfgrass through remote sensing data. The proposed approach leverages various remote sensing techniques, including satellite imagery, unmanned aerial vehicles (UAVs), and ground-based sensors, to gather high-resolution data on turfgrass health, moisture levels, and growth patterns. These data sources feed into a machine learning pipeline, comprising data preprocessing, feature engineering, and algorithm selection, to develop predictive models for turfgrasses. Our findings demonstrate that machine learning models, when trained on remote sensing data, can accurately predict turfgrass parameters that can be used to ensure continuous improvement in turfgrass logistics. The integration of machine learning into turfgrass logistics not only enhances resource utilization but also reduces environmental impact by minimizing unnecessary inputs. We present case studies from various landscapes, including sports fields and golf courses showcasing the practicality and adaptability of our approach.
Keywords: Turfgrass logistics, vegetation indices, machine learning, deep learning, remote sensing© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.