REMOTE SENSING TO UAV-BASED DIGITAL FARMLAND

被引：0

作者：

Falco, Nicola ^{[1
]}

Wainwright, Haruko ^{[1
]}

Ulrich, Craig ^{[1
]}

Dafflon, Baptiste ^{[1
]}

Hubbard, Susan S. ^{[1
]}

Williamson, Malcolm ^{[2
]}

Cothren, Jackson D. ^{[2
]}

Ham, Richard G. ^{[2
]}

McEntire, Jay A. ^{[3
]}

McEntire, McClain ^{[3
]}

机构：

[1] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA

[2] Univ Arkansas, Fayetteville, AR 72701 USA

[3] M2 Capital Partners LLC, Austin, TX 78701 USA

来源：

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年

关键词：

Unmanned aerial vehicle (UAV); phenotyping; precision agriculture; image processing; remote sensing; PRECISION AGRICULTURE; FOOD;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study presents preliminary observations of the first year of a crop monitoring experiment occurred in two soybean agriculture fields in the Arkansas delta. The project focuses on developing image processing and data integration techniques for UAV-based images to optimize advanced farm management such as soil microbial amendments. In particular, we present an effective algorithm that can use high-resolution UAV images efficiently to estimate sprout density and plant vigor/health throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas and provide easily interpretable information for a better decision making. We also present an integrative analysis of UAV-data with geophysical data and harvesting data, which shows high correlation between persistent spatial pattern of soil, plant phenology/growth, and crop yield.

引用

页码：5936 / 5939

页数：4

共 13 条

[1]

Aparicio Virginia, 2017, GEOPHYSICAL RES ABST, V19, P2017

[2]

Benediktsson JA, 2018, SIGNALS COMMUN TECHN, P277, DOI 10.1007/978-3-319-66330-2_7

[3] Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle [J].