Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Jan van Aardt"'
Publikováno v:
Sensors, Vol 24, Iss 12, p 3958 (2024)
Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models fro
Externí odkaz:
https://doaj.org/article/33daf672cbb14f1482c6a01502d8e73a
Autor:
Manisha Das Chaity, Jan van Aardt
Publikováno v:
Remote Sensing, Vol 16, Iss 3, p 498 (2024)
Imaging spectroscopy (hyperspectral sensing) is a proven tool for mapping and monitoring the spatial distribution of vegetation species composition. However, there exists a gap when it comes to the availability of high-resolution spatial and spectral
Externí odkaz:
https://doaj.org/article/6ffa72d6a4424de9ab763995be319b72
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4027-4044 (2022)
Leaf area index (LAI) is an established structural variable that reflects the three-dimensional (3-D) leaf layering of vegetation in response to environmental inputs. In this context, unmanned aerial system (UAS) based methods present a new approach
Externí odkaz:
https://doaj.org/article/dbcdd888b94d4d8dbc253cd89035f096
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 3493-3502 (2020)
Understanding and exploiting topographical data via standard machine learning techniques is challenging, mainly due to the large dynamic range of values present in elevation data and the lack of direct relationships between anthropogenic phenomena an
Externí odkaz:
https://doaj.org/article/7952dac093004a52a86fe9ce4a3bf72f
Autor:
Mohammad S. Saif, Robert Chancia, Sarah Pethybridge, Sean P. Murphy, Amirhossein Hassanzadeh, Jan van Aardt
Publikováno v:
Remote Sensing, Vol 15, Iss 3, p 794 (2023)
New York state is among the largest producers of table beets in the United States, which, by extension, has placed a new focus on precision crop management. For example, an operational unmanned aerial system (UAS)-based yield forecasting tool could p
Externí odkaz:
https://doaj.org/article/1ae52a1162eb4c43b7566c1e8d050eed
Publikováno v:
Remote Sensing, Vol 13, Iss 21, p 4489 (2021)
This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutr
Externí odkaz:
https://doaj.org/article/3bd85f3906e44fe091e652ff8c666833
Autor:
Jason B. Cho, Joseph Guinness, Tulsi Kharel, Ángel Maresma, Karl J. Czymmek, Jan van Aardt, Quirine M. Ketterings
Publikováno v:
Agronomy, Vol 11, Iss 10, p 2042 (2021)
On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistic
Externí odkaz:
https://doaj.org/article/93844a93113e41d68c43628df2f6dd05
Publikováno v:
Remote Sensing, Vol 13, Iss 19, p 3975 (2021)
The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds
Externí odkaz:
https://doaj.org/article/99c8678353c5436fbaf2d8dbe73f2554
Publikováno v:
Remote Sensing, Vol 13, Iss 19, p 3948 (2021)
Harvester-mounted yield monitor sensors are expensive and require calibration and data cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system (Quantix™ Mapper) imagery for corn (Zea mays L.) yield prediction. A fi
Externí odkaz:
https://doaj.org/article/3c0860db048d448080fa394247efd205
Publikováno v:
Remote Sensing, Vol 13, Iss 16, p 3241 (2021)
Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we
Externí odkaz:
https://doaj.org/article/bd2e5fbe1daf4bb2b718ecd13aa2002b