Proceedings
Authors
| Filter results16 paper(s) found. |
|---|
1. Ultra Low Level Aircraft (ULLA) As A Platform For Active Optical Sensing Of Crop BiomassCrop producers requiring crop biomass maps to support timely application of in-season fertilisers, pesticides or growth regulators rely on either on-ground active sensors or airborne/satellite imagery. Active crop sensing (for example using Yara N-SensorTM, GreenseekerTM or CropcircleTM) can only be used when the crop is accessible by person or vehicle, and extensive, high-resolution coverage is time consuming. On the other hand, airborne or satellite imaging is... D.W. Lamb, M.G. Trotter, D. Schneider |
2. Weeds Detection By Ground-level Hyperspectral ImagingWeeds 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 |
3. Assessment Of Field Crops Leaf Area Index By The Red-edge Inflection Point Derived From Venus BandsThe 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 |
4. Multi, Super Or Hyper Spectral Data, The Right Way From Research Toward Application In AgricultureRemote 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 |
5. Precision Livestock Management: An Example Of Pasture Monitoring In Eastern Australian Pastures Using Proximal And Remote Sensing ToolsPasture monitoring Australian rangelands by Remote Sensing G.E.Donald. CSIRO Livestock Industries, Locked Bag 1, Armidale NSW, 2350 Australia A series of spatial models and datasets were jointly developed to estimate pasture biomass as feed on offer (FOO®) and pasture growth rate (PGR®) in the south-west... G.E. Donald, M.G. Trotter, D.W. Lamb, G. Levow, H.M. Van es |
6. Matching Nitrogen To Plant Available Water For Malting Barley On Highly Constrained Vertosol SoilCrop 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. GNSS Tracking Of Livestock: Towards Variable Fertilizer Strategies For The Grazing IndustryThis study reveals the potential for GPS tracking in the grazing industry. By monitoring the locations and movement of livestock, times of peak grazing activity can be identified and these can in turn produce maps of preferred grazing areas, and by examining residency times provide an indication of spatial variability in grazing pressure. A comparison of grazing preference can be made to similarly inferred camping areas to understand the potential redistribution of nutrients within a paddock.... M.G. Trotter, D.W. Lamb, G.N. Hinch, C.N. Guppy |
8. Ground Level Hyperspectral Imagery For Weeds Detection In Wheat FieldsWeeds 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 |
9. Spatial Apparent Electrical Conductivity (ECa), Soil Moisture and Water Use Efficiency in Vertosol SoilsProducing high resolution maps of water use efficiency (crop yield per unit of water consumption; WUE) for precision crop management is limited by our ability to readily produce maps of soil moisture... J.N. Stanley, D.A. Schneider, D.W. Lamb |
10. Airborne Active Optical Sensors (AOS) For Photosynthetically-Active Biomass Sensing: Current Status And Future OpportunitiesThe first published deployment of an active optical reflectance sensor (AOS) in a low-flying aircraft in 2009 catalyzed numerous developments in both sensor development and sensor platform integration. Integral to these sensors is a modulated light source composed of high power LED technology that emits high radiance polychromatic light. The sensor easily mounts to agricultural aircraft and can sense agricultural landscapes at altitudes from a few meters to altitudes exceeding 40 meters while... K.H. Holland, D.W. Lamb |
11. Rapidscan And CropCircle Radiometers: Opportunities And Limitation In Assessing Wheat Biomass And NitrogenRemote 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 |
12. Winter Wheat Genotype Effect on Canopy Reflectance: Implications for Using NDVI for In-season Nitrogen Topdressing RecommendationsActive optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditions... O.S. Walsh, S.M. Samborski, M. Stępień, D. Gozdowski, D.W. Lamb, E.S. gacek, T. Drzazga |
13. Ear Deployed Accelerometer Behaviour Detection in SheepAn animal’s behaviour can be a clear indicator of their physiological and physical state. Therefore as resting, eating, walking and ruminating are the predominant daily activities of ruminant animals, monitoring these behaviours could provide valuable information for management decisions and individual animal health status. Traditional animal monitoring methods have relied on human labor to visually observe animals. Accelerometer technology offers the possibility of remotely monitoring animal... J.D. Barwick, M. Trotter, D.W. Lamb, R. Dobos, M. Welch |
14. Enhancing PA Adoption Through Value ConnectionsDespite an increase in breadth of precision agriculture over time, and the attendant elements of digital agriculture that either support PA or integrates the outputs of PA, the pace of adoption of digital agriculture in our farming systems remains slow. In assessing impediments to adoption of digital agriculture, much work to date has focused on the value proposition as considered by individual producers or value chain actors. At this level, adoption remains constrained by perceptions of... D.W. Lamb, M.T. Schaefer |
15. 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 |
16. Utilizing Image-based Artificial Intelligence for Grading Bovine OocytesFor years, proper oocyte selection has been carried out with the precision of a lab technician’s eyes. The classification of oocytes using image-based artificial intelligence is a new technology that IVF lab technicians, cattle genetics companies, and veterinarians can utilize. Via the aspiration of the follicles on a cow’s ovaries, oocytes are able to be collected. Once oocytes are obtained from the ovaries of a cow, they are sent to an IVF lab to be cleaned and evaluated by a lab... G. Koppelman, J.P. Fulton, S. Khanal, T. Berger-wolf |