The importance of tree cultivation and management is necessary for the 21st century, given the need to sequestrate carbon and secure adequate food and raw materials productivity, which are part of the ecosystem services trees provide. This study improves upon previous studies and bridges the gap in assessing the roots of trees using non-invasive approaches. This study assessed the root distribution of Tart cherry trees using ground-penetrating radar (GPR) and artificial intelligence. Grid and cylindrical data collection and processing methodology were employed using the 800 MHz antenna frequency. Three mature trees were sampled from two Tart cherry fields in Michigan State (Clarksville and Traverse City). The reconstruction results revealed that the roots extend 30-45 cm deep in the soil. Furthermore, an Unmanned Aerial Vehicle (Matrix 100 drone) was used to obtain RGB aerial images from both fields.The findings of this study show that Tart cherry tree roots extend farther than the canopy size, as discussed extensively in this Thesis. A controlled experiment was developed to serve as ground truth in assessing the GPR's accuracy. The reconstructed result showed that the GPR accurately reconstructed and measured the depth the proxies were buried and the length of the root proxies. The biomass weight model estimator was another novel idea developed in this study. The model was developed using 115 root proxies, where the measured biomass length, width, and circumference were used as independent variables in predicting the weight of the biomass. Four regressor algorithms were used in developing the weight model. 5-fold cross-validation showed that the model performed optimally with an error of about 6% in the weight prediction. This study highlights the potential of GPR and artificial intelligence in assessing root distribution in Tart cherry trees, offering valuable insights for optimizing tree management and growth.
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