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dc.contributor.authorRaja, Rekha
dc.contributor.authorSlaughter, David C.
dc.contributor.authorFennimore, Steven A.
dc.contributor.authorNguyen, Thuy T.
dc.contributor.authorVuong, Vivian L.
dc.contributor.authorSinha, Neelima
dc.contributor.authorTourte, Laura
dc.contributor.authorSmith, Richard F.
dc.contributor.authorSiemens, Mark C.
dc.date.accessioned2019-12-18T18:20:03Z
dc.date.available2019-12-18T18:20:03Z
dc.date.issued2019-10-10
dc.identifier.citationRaja, R., Slaughter, D. C., Fennimore, S. A., Nguyen, T. T., Vuong, V. L., Sinha, N., ... & Siemens, M. C. (2019). Crop signalling: A novel crop recognition technique for robotic weed control. Biosystems Engineering, 187, 278-291.en_US
dc.identifier.issn1537-5110
dc.identifier.doi10.1016/j.biosystemseng.2019.09.011
dc.identifier.urihttp://hdl.handle.net/10150/636446
dc.description.abstractWeed control is a significant cost for speciality crop producers, especially on organic farms. Agricultural operations are still largely dependent on hand weeding that is labour intensive and labour shortages and rising wages have led to a surge in food production costs. Thus, there is an inherent need to automate weed control and contain both labour costs and demands. Automatically distinguishing weeds from the crop plant is a complex problem since weeds come in a wide variety of colours, shapes, and sizes, and crop plant foliage is often overlapped with itself or occluded by the weeds. Current technology in commercial use, cannot reliably and effectively perform the differentiation task in such complex scenarios in real-time. As a solution to this problem, our team at the University of California, Davis has developed a novel concept called crop signalling, a technology to make crop plants machine readable and reliably distinguishable from weeds for automatic weed control. Four different techniques have been investigated and developed to make smart crop marking systems such as a) systemic markers, b) fluorescent proteins, c) plant labels and d) topical markers. Indoor experiments have been conducted for each method. Field experiments, using plant labels and the topical markers methods, have been successfully conducted for real-time weed control in tomato and lettuce. The results demonstrated that robots could automatically detect and distinguish 99.7% of the crop plants with no false positive errors in dense complex outdoor scenes with high weed densities. The crop/weed differentiation was thus effective, fast, reliable, and commercialisation of robotic weed control using the technique may be feasible.en_US
dc.description.sponsorshipUSDA NIFA Speciality Crops Research Initiative [USDA-NIFA-SCRI-004530]; California Tomato Research Institute; California Leafy Greens Research Programen_US
dc.language.isoenen_US
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCEen_US
dc.rightsCopyright © 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.en_US
dc.subjectControl and Systems Engineeringen_US
dc.subjectAgronomy and Crop Scienceen_US
dc.subjectFood Scienceen_US
dc.subjectAnimal Science and Zoologyen_US
dc.subjectSoil Scienceen_US
dc.titleCrop signalling: A novel crop recognition technique for robotic weed controlen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Biosyst Engnen_US
dc.identifier.journalBIOSYSTEMS ENGINEERINGen_US
dc.description.note24 month embargo; published online: 10 October 2019en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.volume187
dc.source.beginpage278-291


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