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Bonfil, D.J
Martin, R
Delgado, J.A
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Authors
Bonfil, D.J
Shapira, U
Karnieli, A
Herrmann, I
Kinast, S
Delgado, J.A
Ascough II, J.C
Delgado, J.A
Bonfil, D.J
Herrmann, I
Pimstein, A
Karnieli, A
Shapira , U
Herrmann, I
Karnieli, A
Bonfil, D.J
Herrmann, I
Pimstein, A
Karnieli, A
Cohen, Y
Alchanatis , V
Bonfil, D.J
Cointault, F
Marin, A
Journaux, L
Miteran, J
Martin, R
Sauer, B
Guppy, C.N
Trotter, M.G
Lamb, D.W
Delgado, J.A
Gitelson, A.A
Bonfil, D.J
Rozenstein, O
Cohen, Y
Alchanatis , V
Behrendt, K
Bonfil, D.J
Eshel, G
Harari, A
Harris, W.E
Klapp, I
Laor, Y
Linker, R
Paz-Kagan, T
Peets, S
Rutter, M.S
Salzer, Y
Lowenberg-DeBoer, J
Topics
Remote Sensing Applications in Precision Agriculture
Precision Conservation and Carbon Management
Precision Conservation
Remote Sensing Applications in Precision Agriculture
Modeling and Geo-statistics
Precision Nutrient Management
Sensor Application in Managing In-season CropVariability
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Type
Poster
Oral
Year
2012
2010
2014
2024
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Authors

Filter results10 paper(s) found.

1. A New GIS Approach To Assess Nitrogen Management Across The USA

Nitrogen is one of the elements that are essential to maximizing agricultural productivity and economic returns for farmers. Its management is difficult because this element is very dynamic and mobile, characteristics that can contribute to significant losses via atmospheric, surface and/or leaching pathways. The magnitude of these losses can be affected by site-specific physical and chemical factors. These physical and chemical factors can vary significantly across the landscape, adding to the... J. Delgado

2. Multi, Super Or Hyper Spectral Data, The Right Way From Research Toward Application In Agriculture

Remote sensing provides opportunities for diverse applications in agriculture. One consideration of maximizing the utility of these applications, is the need to choose the most efficient spectral resolution. Picking the optimal spectral resolutions (multi, super or hyper) for a specific application is also influenced by other factors (e.g., spatial and temporal resolutions) of the utilized device. This work focuses mainly on... D.J. Bonfil, I. Herrmann, A. Pimstein, A. Karnieli

3. Weeds Detection By Ground-level Hyperspectral Imaging

Weeds are a severe pest in agriculture, causing extensive yield loss. Weed control of grass and broadleaf weeds is commonly performed by applying selective herbicides homogeneously all over the field. As presented in several studies, applying the herbicide only where needed has economical as well as environmental benefits. Combining remote sensing tools and techniques with the concept of precision agriculture has the potential to automatically... U. Shapira , I. Herrmann, A. Karnieli, D.J. Bonfil

4. Assessment Of Field Crops Leaf Area Index By The Red-edge Inflection Point Derived From Venus Bands

The red-edge region of leaves spectrum (700-800 nm) corresponds to the spectral region that connects the chlorophyll absorption in the red and the amplified reflectance caused by the leaf structure in the near infrared (NIR) parts of the spectrum. At the canopy level, the inflection point of the red-edge slope is influenced by the plant’s condition that is related to several properties, including Leaf Area Index (LAI) and plant nutritional status.... I. Herrmann, A. Pimstein, A. Karnieli, Y. Cohen, V. Alchanatis , D.J. Bonfil

5. Wheat Growth Stages Discrimination Using Generalized Fourier Descriptors In Pattern Recognition Context

... F. Cointault, A. Marin, L. Journaux, J. Miteran, R. Martin

6. Matching Nitrogen To Plant Available Water For Malting Barley On Highly Constrained Vertosol Soil

Crop yield monitoring, high resolution aerial imagery and electromagnetic induction (EMI) soil sensing are three widely used techniques in precision agriculture (PA). Yield maps provide an indication of the crop’s response to a particular management regime in light of spatially-variable constraints. Aerial imagery provides timely and accurate information about photosynthetically-active biomass during crop growth and EMI indicates spatial variability in soil texture, salinity and/or... B. Sauer, C.N. Guppy, M.G. Trotter, D.W. Lamb, J.A. Delgado

7. Ground Level Hyperspectral Imagery For Weeds Detection In Wheat Fields

Weeds are a severe pest in agriculture resulting in extensive yield loss. Applying precise weed control has economical as well as environmental benefits. Combining remote sensing tools and techniques with the concept of precision agriculture has the potential to automatically locate and identify weeds in order to allow precise control. The objective of the current work is to detect annual... D.J. Bonfil, U. Shapira, A. Karnieli, I. Herrmann, S. Kinast

8. A New Version of the Nitrogen Trading Tool (NTT) To Assess Nitrogen Management across the USA

A recent study from the USDA Economic Research Service (September 2011) reported that about one-third of U.S. cropland was found to meet the requirements for nutrient... J.A. Delgado, J.C. Ascough ii

9. Rapidscan And CropCircle Radiometers: Opportunities And Limitation In Assessing Wheat Biomass And Nitrogen

Remote sensing is a promising technology that provides information about the crop's physiological and phenological status. This information is based on the spectral absorption and scattering features of the plants. Many different vegetation indices (VI) have been developed, and are in use to estimate quantitatively the relationship between multi and hyper-spectral reflectance and effective crop physiological parameters, i.e. nitrogen (N) content, biomass, leaf area index (LAI). The CropCircle... A.A. Gitelson, D.J. Bonfil

10. Data-driven Agriculture and Sustainable Farming: Friends or Foes?

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive.  It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience.  Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer