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Combining Machine Learning with Computer Vision for Precision Agriculture Applications

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서명/저자사항Combining Machine Learning with Computer Vision for Precision Agriculture Applications.
개인저자Zermas, Dimitris.
단체저자명University of Minnesota. Computer Science.
발행사항[S.l.]: University of Minnesota., 2018.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2018.
형태사항106 p.
기본자료 저록Dissertation Abstracts International 79-10B(E).
Dissertation Abstract International
ISBN9780438031289
학위논문주기Thesis (Ph.D.)--University of Minnesota, 2018.
일반주기 Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Nikolaos Papanikolopoulos.
요약Financial and social elements of modern societies are closely connected to the cultivation of corn. Due to its massive production, deficiencies during the cultivation process directly translate to major financial losses. Existing field monitorin
요약First, we propose a methodology to detect nitrogen (N) deficiencies in corn fields and assess their severity at an early stage using low-cost RGB sensors. The introduced methodology is twofold. First, a low complexity recommendation scheme ident
요약Second, based on the 3D reconstruction of small batches of corn plants at growth stages between ''V3'' and ''V6'', an automated alternative to existing manual and cumbersome phenotype estimation methodologies is presented. The use of 3D models p
요약Although the proposed methodologies are agnostic to the platform that performs the data collection, for the presented experiments a MikroKopter Okto XL equipped with a Nikon D7200 RGB sensor and a DJI Matrice 100 with a Zenmuse X3 and a Zenmuze
요약Thorough data collection and interpretation leads to a better understanding of the needs not only of the farm as a whole but to each individual plant providing a much higher granularity to potential treatment strategies. Through the thoughtful u
일반주제명Agricultural engineering.
Artificial intelligence.
Plant pathology.
언어영어
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