Proceedings

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Colaço, A.F
Laor, Y
Rudolph, S
Herrmann, I
Lew, D
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Authors
Bonfil, D.J
Shapira, U
Karnieli, A
Herrmann, I
Kinast, S
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
Sanchez, L.A
Klein, L.J
Claassen, A
Lew, D
Mendez-Costabel, M
Sams, B
Morgan, A
Hinds, N
Hamann, H.F
Dokoozlian, N
Rudolph, S
Marchant, B.P
Gillingham, V
Kindred, D
Sylvester-Bradley, R
Canata, T.F
Molin, J.P
Colaço, A.F
Trevisan, R.G
Fiorio, P.R
Martello, M
Colaço, A.F
Molin, J.P
Trevisan, R.G
Rosell-Polo, J.R
Escolà, A
Herrmann, I
Vosberg, S
Ravindran, P
Singh, A
Townsend, P
Conley, S
Sahoo, M
Tarshish, R
Alchanatis , V
Herrmann, I
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
Remote Sensing Applications in Precision Agriculture
Precision Horticulture
Big Data Mining & Statistical Issues in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Precision Horticulture
Precision Agriculture and Global Food Security
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2024
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Filter results11 paper(s) found.

1. 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

2. 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

3. 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

4. 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

5. Effect Of A Variable Rate Irrigation Strategy On The Variability Of Crop Production In Wine Grapes In California

Pruning and irrigation are the cultural practices with the highest potential impact on yield and quality in wine grapes. In particular, irrigation start date, rates and frequency can be synchronized with crop development stages to control canopy growth and, in turn, positively influence light microclimate, berry size and fruit quality. In addition, canopy management practices can be implemented in vineyards with large canopies to ensure fruit zone microclimate... L.A. Sanchez, L.J. Klein, A. Claassen, D. Lew, M. Mendez-costabel, B. Sams, A. Morgan, N. Hinds, H.F. Hamann, N. Dokoozlian

6. 'Spatial Discontinuity Analysis' a Novel Geostatistical Algorithm for On-farm Experimentation

Traditional agronomic experimentation is restricted to small plots. Under appropriate experimental designs the effects of uncontrolled environmental variables are minimized and the measured responses (e.g. in yields) are compared to controllable inputs (seed, tillage, fertilizer, pesticides) using well-trusted design-based statistical methods. However, the implementation of such experiments can be complex and the application, management, and harvesting of treated areas might have to... S. Rudolph, B.P. Marchant, V. Gillingham, D. Kindred, R. Sylvester-bradley

7. Measuring Height of Sugarcane Plants Through LiDAR Technology

Sugarcane (Saccharum spp.) has an important economic role in Brazilian agriculture, especially in São Paulo State. Variation in the volume of plants can be an indicative of biomass which, for sugarcane, strongly relates to the yield. Laser sensors, like LiDAR (Light Detection and Ranging), has been employed to estimate yield for corn, wheat and monitoring forests. The main advantage of using this type of sensor is the capability of real-time data acquisition in a non-destructive way, previously... T.F. Canata, J.P. Molin, A.F. Colaço, R.G. Trevisan, P.R. Fiorio, M. Martello

8. Spatial Variability of Canopy Volume in a Commercial Citrus Grove

LiDAR (light detection and ranging) sensors have shown good potential to estimate canopy volume and guide variable rate applications in different fruit crops. Oranges are a major crop in Brazil; however the spatial variability of geometrical parameters remains still unknown in large commercial groves, as well as the potential benefit of sensor guided variable rate applications. Thus, the objective of this work was to characterize the spatial variability of the canopy volume in a commercial orange... A.F. Colaço, J.P. Molin, R.G. Trevisan, J.R. Rosell-polo, A. Escolà

9. Exploring Tractor Mounted Hyperspectral System Ability to Detect Sudden Death Syndrome Infection and Assess Yield in Soybean

Pre-visual detection of crop disease is critical for both food and economic security. The sudden death syndrome (SDS) in soybeans, caused by Fusarium virguliforme (Fv), induces 100 million US$ crop loss, per year, in the US alone. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were measured at canopy level over a course of a growing season to assess the capacity of spectral measurements... I. Herrmann, S. Vosberg, P. Ravindran, A. Singh, P. Townsend, S. Conley

10. Comparing Hyperspectral and Thermal UAV-borne Imagery for Relative Water Content Estimation in Field-grown Sesame

Sesame (Sesamum indicum) is an irrigated oilseed crop, and studies on its water content estimation are sparred. Unmanned aerial vehicle (UAV)-borne imageries using spectral reflectance as well as thermal emittance for crops are an ample source of high throughput information about their physiological and chemical traits. Though several studies have dealt with thermal emittance to assess the crop water content, evaluating its relation to the plant’s solar reflectance is limitedly... M. Sahoo, R. Tarshish, V. Alchanatis , I. Herrmann

11. 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