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dc.contributor.authorRamirez, Rachael Angelaen_US
dc.date.accessioned2014-03-14T20:42:30Z
dc.date.available2014-03-14T20:42:30Z
dc.date.issued2006-07-25en_US
dc.identifier.otheretd-08022006-185828en_US
dc.identifier.urihttp://hdl.handle.net/10919/34311
dc.description.abstractAs technology advances in all areas of society and industry, the technology used to produce one of life's essentials - food - is also improving. The majority of agriculture production in developed countries has gone from family farms to industrial operations. With the advent of large-scale farming, the automation of basic farming operations has increasingly made practical and economic sense. Broccoli, which is still harvested by hand, is one of the most expensive crops to produce. Investing in sensing technology that can provide detailed information about the location, maturity and viability of broccoli heads has the potential to produce great commercial benefits. This technology is also a prerequisite for developing an autonomous harvester that could select and harvest mature heads of broccoli. This thesis details the work done to develop a computer vision algorithm that has the ability to locate the broccoli head within an image of an entire broccoli plant and to distinguish between mature and immature broccoli heads. Locating the head involves the use of a Hough transform to find the leaf stems and, once the stems are found, the location and extent of the broccoli head can be ascertained with the use of contrast texture analysis at the intersection of the stems. A co-occurrence matrix is then produced of the head and statistical texture analysis is performed to determine the maturity of the broccoli head. The conceptual design of a selective autonomous broccoli harvester, as well as suggestions for further research, is also presented.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartRamirezThesis1.pdfen_US
dc.relation.haspartRamirezThesis2.pdfen_US
dc.relation.haspartRamirezThesis.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectautonomous vehicleen_US
dc.subjectmechanical harvesteren_US
dc.subjectcomputer visionen_US
dc.subjectbroccolien_US
dc.subjectco-occurrence matrixen_US
dc.subjecttexture analysisen_US
dc.titleComputer Vision Based Analysis of Broccoli for Application in a Selective Autonomous Harvesteren_US
dc.typeThesisen_US
dc.contributor.departmentMechanical 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.disciplineMechanical Engineeringen_US
dc.contributor.committeechairReinholtz, Charles F.en_US
dc.contributor.committeememberNowak, Jerzyen_US
dc.contributor.committeememberKachroo, Pushkinen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08022006-185828/en_US
dc.date.sdate2006-08-02en_US
dc.date.rdate2006-10-06
dc.date.adate2006-10-06en_US


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