Automatic canopy plot boundary detection using computer vision
| dc.contributor.advisor | King, Scott A. | |
| dc.contributor.advisor | Gonzales, Xavier | |
| dc.contributor.author | Wynn, De Kwaan | |
| dc.contributor.committeeMember | Li, Longzhuang | |
| dc.date.accessioned | 2020-12-18T22:51:45Z | |
| dc.date.available | 2020-12-18T22:51:45Z | |
| dc.date.issued | 2020-05 | |
| dc.description.abstract | In Corpus Christi, Texas the United States Department of Agricultre (USDA) funded Texas A&M Agrilife research on large farmlands with hundreds of individual cotton vegetation plots. Each plot is planted uniformly in rows but not all plots grow at the same rate. Every week the plots are photographed using an Unmanned Aircraft System (UAS) flying at a height of 100 feet to record and evaluate growth for various reasons. The research scientists and farmer’s current method of localizing individual plots of vegetation within an image has proven to be very time consuming and inefficient. The algorithm developed in this paper automates the localization process under sunny conditions. The algorithm uses the Hue, Saturation, and Value (HSV) color space during the preprocessing stage to provide a binary image that indicates where each green pixel is located. Various OpenCV functions are then used to automate the crop localization process. Minimum and Maximum threshold values are set for filtering by size sections of the algorithm. Then, morphological operations are employed to further refine the regions of interest. The Connected Components function is used to determine how large each remaining object is and that size is then used to determine how large each localizing polygon will be drawn. After the size of each object is found, the size of each localizing polygon to be drawn is evaluated and split into smaller polygons whenever necessary. Not only the developed algorithm able to detect and classify cotton crop locations quickly but it is able to handle various complex situations. The developed method was evaluated by its accuracy, precision, and recall, which were 92.4%, 100%, and 92.4% respectively. | en_US |
| dc.description.college | College of Science and Engineering | en_US |
| dc.description.department | Computing Sciences | en_US |
| dc.format.extent | 82 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1969.6/89245 | |
| dc.language.iso | en | en_US |
| dc.rights | This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher. | en_US |
| dc.title | Automatic canopy plot boundary detection using computer vision | en_US |
| dc.type | Text | en_US |
| dc.type.genre | Thesis | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | Texas A & M University--Corpus Christi | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |