자료유형 | 학위논문 |
---|---|
서명/저자사항 | Combining Machine Learning with Computer Vision for Precision Agriculture Applications. |
개인저자 | Zermas, Dimitris. |
단체저자명 | University of Minnesota. Computer Science. |
발행사항 | [S.l.]: University of Minnesota., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 106 p. |
기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
ISBN | 9780438031289 |
학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Nikolaos Papanikolopoulos. |
요약 | Financial 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 |
요약 | First, 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 |
요약 | Second, 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 |
요약 | Although 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 |
요약 | Thorough 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 |
일반주제명 | Agricultural engineering. Artificial intelligence. Plant pathology. |
언어 | 영어 |
바로가기 |
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |