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dc.contributor.authorDhami, Harnaik Singhen_US
dc.date.accessioned2019-09-10T08:01:07Z
dc.date.available2019-09-10T08:01:07Z
dc.date.issued2019-09-09
dc.identifier.othervt_gsexam:22345en_US
dc.identifier.urihttp://hdl.handle.net/10919/93513
dc.description.abstractIn this thesis, we develop algorithms to estimate crop heights as well as to detect plots infarms. Plant height estimation is needed in precision agriculture to monitor plant health andgrowth cycles. We use a 3D LiDAR mounted on an Unmanned Aerial Vehicle (UAV) anduse the LiDAR data for height and plot estimation. We present a general methodology forextracting plant heights from 3D LiDAR with two specific variants for the two environments:row-crops and pasture. The main algorithm is based on ground plane estimation from 3DLiDAR scans, which is then used to determine the height of plants in the scans. For rowcrops, the plot detection uses a K-means clustering algorithm to find the bounding boxes ofthese clusters, and a voting scheme to determine the best-fit width, height, and orientationof the clusters/plots. This best-fit box is then used to create a grid over the LiDAR dataand the plots are extracted. For pasture, relative heights are estimated using data collectedweekly. Both algorithms we evaluated using data collected from actual farms and pasture.The accuracy in plot height estimation was +/- 5.36 % and that for growth estimates was+/- 7.91 %.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectLiDARen_US
dc.subjectPlant Height Estimationen_US
dc.subjectPlot Detectionen_US
dc.subjectK-meansen_US
dc.subjectPrecisionAgricultureen_US
dc.titleUsing UAV Mounted LiDAR to Estimate Plant Height and Growthen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairTokekar, Pratapen_US
dc.contributor.committeememberWilliams, Ryan K.en_US
dc.contributor.committeememberLi, Songen_US
dc.description.abstractgeneralPlant height estimation and measurement is a vital task when it comes to farming. Knowing these characteristics help determine whether the plants are growing healthy and when to harvest them. On similar lines, accurate estimates of the plant heights can be used to prevent overgrazing and undergrazing of pastures. However, as farm and plot size increases, getting consistent and accurate measurements becomes a more time-consuming and manually intensive task. Using robots can help solve this problem because they can be used to estimate the height. With sensors that are already available, such as the 3D LiDAR that we use, we can use aerial robots to fly over the farm and collect plant data. This data can then be processed to estimate the plant height, eliminating the need to go out and manually measure every single plant. This thesis discusses a methodology of doing exactly this, as well as detecting plots within a farm. The algorithms are evaluated using data collected from actual farms and pasture.en


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