Unmanned Aerial System for Monitoring Crop Status
| dc.contributor.author | Rogers, Donald Ray III | en |
| dc.contributor.committeechair | Kochersberger, Kevin B. | en |
| dc.contributor.committeemember | Bird, John P. | en |
| dc.contributor.committeemember | Nowak, Jerzy | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2014-01-12T09:00:28Z | en |
| dc.date.available | 2014-01-12T09:00:28Z | en |
| dc.date.issued | 2014-01-11 | en |
| dc.description.abstract | As the cost of unmanned aerial systems (UAS) and their sensing payloads decrease the practical applications for such systems have begun expanding rapidly. Couple the decreased cost of UAS with the need for increased crop yields under minimal applications of agrochemicals, and the immense potential for UAS in commercial agriculture becomes immediately apparent. What the agriculture community needs is a cost effective method for the field-wide monitoring of crops in order to determine the precise application of fertilizers and pesticides to reduce their use and prevent environmental pollution. To that end, this thesis presents an unmanned aerial system aimed at monitoring a crop's status. The system presented uses a Yamaha RMAX unmanned helicopter, operated by Virginia Tech']s Unmanned Systems Lab (USL), as the base platform. Integrated with helicopter is a dual-band multispectral camera that simultaneously captures images in the visible and near-infrared (NIR) spectrums. The UAS is flown over a quarter acre corn crop undergoing a fertilizer rate study of two hybrids. Images gathered by the camera are post-processed to form a Normalized Difference Vegetative Index (NDVI) image. The NDVI images are used to detect the most nutrient deficient corn of the study with a 5% margin of error. Average NDVI calculated from the images correlates well to measured grain yield and accurately identifies when one hybrid reaches its yield plateau. A secondary test flight over a late-season tobacco field illustrates the system's capabilities to identify blocks of highly stressed crops. Finally, a method for segmenting bleached tobacco leaves from green leaves is presented, and the segmentation results are able to provide a reasonable estimation of the bleached tobacco content per image. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:1960 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/24811 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | UAS | en |
| dc.subject | Crop Monitoring | en |
| dc.subject | Precision Agriculture | en |
| dc.subject | Multispectral | en |
| dc.subject | NDVI | en |
| dc.title | Unmanned Aerial System for Monitoring Crop Status | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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