| LDR | | 02743nam u200469 4500 |
| 001 | | 000000420640 |
| 005 | | 20190215164634 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438031289 |
| 035 | |
▼a (MiAaPQ)AAI10785826 |
| 035 | |
▼a (MiAaPQ)umn:19067 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 631 |
| 100 | 1 |
▼a Zermas, Dimitris. |
| 245 | 10 |
▼a Combining Machine Learning with Computer Vision for Precision Agriculture Applications. |
| 260 | |
▼a [S.l.]:
▼b University of Minnesota.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 106 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Nikolaos Papanikolopoulos. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Agricultural engineering. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Plant pathology. |
| 690 | |
▼a 0539 |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0480 |
| 710 | 20 |
▼a University of Minnesota.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0130 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997327
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |