Physical and structural tree measurements are applied in forestry, precision agriculture and conservation for various reasons. Since measuring tree properties manually is tedious, measurements from only a small subset of trees present in a forest, agricultural land or survey site are often used. Utilizing robotics to autonomously estimate physical tree…
Physical and structural tree measurements are applied in forestry, precision agriculture and conservation for various reasons. Since measuring tree properties manually is tedious, measurements from only a small subset of trees present in a forest, agricultural land or survey site are often used. Utilizing robotics to autonomously estimate physical tree dimensions would speed up the measurement or data collection process and allow for a much larger set of trees to be used in studies. In turn, this would allow studies to make more generalizable inferences about areas with trees. To this end, this thesis focuses on developing a system that generates a semantic representation of the topology of a tree in real-time. The first part describes a simulation environment and a real-world sensor suite to develop and test the tree mapping pipeline proposed in this thesis. The second part presents details of the proposed tree mapping pipeline. Stage one of the mapping pipeline utilizes a deep learning network to detect woody and cylindrical portions of a tree like trunks and branches based on popular semantic segmentation networks. Stage two of the pipeline proposes an algorithm to separate the detected portions of a tree into individual trunk and branch segments. The third stage implements an optimization algorithm to represent each segment parametrically as a cylinder. The fourth stage formulates a multi-sensor factor graph to incrementally integrate and optimize the semantic tree map while also fusing two forms of odometry. Finally, results from all the stages of the tree mapping pipeline using simulation and real-world data are presented. With these implementations, this thesis provides an end-to-end system to estimate tree topology through semantic representations for forestry and precision agriculture applications.
Precise addition of agricultural inputs to maximize yields, especially in the face of environmental stresses, becomes important from the financial and sustainability perspectives. Given compounding factors such as climate change and disputed water claims in the American Southwest, the ability to build resistance against salinity stress becomes especially important. It…
Precise addition of agricultural inputs to maximize yields, especially in the face of environmental stresses, becomes important from the financial and sustainability perspectives. Given compounding factors such as climate change and disputed water claims in the American Southwest, the ability to build resistance against salinity stress becomes especially important. It was evaluated if an algal bio-fertilizer was able to remediate salinity stress in Solanum Lycopersicum. A hydroponic apparatus was employed, and data from Burge Environmental’s MiProbes™ both were able to demonstrate remediation. Future research could include determining the minimum dosage of algal fertilizer sufficient to induce this result, or the maximum concentration of salt that an algal treatment can provide a protective effect against.
