Proceedings

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Castell, A
Cline, V
Han, M
Hannah, L
Cohen, Y
Cao, W
Colaço, A
Olfs, H
Harris, G
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Authors
Borchert, A
Trautz, D
Olfs, H
Borchert, A
Recke, G
Dabbelt, D
Trautz, D
Olfs, H
Nigon, T.J
Rosen, C
Mulla, D
Cohen, Y
Alchanatis, V
Rud, R
Cohen, Y
Alchanatis, V
Heuer, B
Lemcoff, H
Sprintsin, M
Rosen, C
Mulla, D
Nigon, T
Dar, Z
Cohen, A
Levi, A
Brikman, R
Markovits, T
Rud, R
Cohen, Y
Alchanatis, V
Levi, O
Cohen, S
Herrmann, I
Pimstein, A
Karnieli, A
Cohen, Y
Alchanatis , V
Bonfil, D.J
Rice, K
Carson, T
Krum, J
Flitcroft, I
Cline, V
Carrow, R
Olfs, H
Trautz, D
Borchert, A
Unamunzaga, O
Castell, A
Besga, G
Perez-Parmo, R
Aizpurua, A
Alchanatis, V
Cohen, Y
Sprinstin, M
Cohen, A
Zipori, I
Dag, A
Naor, A
Rosenberg, O
Alchanatis, V
Saranga, Y
Bosak, A
Cohen, Y
Anselmi, A.A
Molin, J.P
eitelwein, M.T
Trevisan, R
Colaço, A
Meron, M
Tsipris, J
Orlov, V
Alchnatis, V
Cohen, Y
Liu, X
Cao, Q
Tian, Y
Zhu, Y
Zhang, Z
Cao, W
KC, K
Hannah, L
Roehrdanz, P
Donatti, C
Fraser, E
Berg, A
Saenz, L
Wright, T.M
Hijmans, R.J
Mulligan, M
Goldwasser, Y
Alchanati, V
Goldshtein, E
Cohen, Y
Gips, A
Nadav, I
Katz, L
Ben-Gal, A
Litaor, I
Naor, A
Peeters, A
Goldshtein, E
Alchanatis, V
Cohen, Y
Tucker, M.W
Virk, S
Harris, G
Lessl, J
Levi, M
Miao, Y
liu, X
Tian, Y
Zhu, Y
Cao, W
Cao, Q
Chen, X
Li, Y
Zhang, J
Wang, W
Fu, Z
Cao, Q
Tian, Y
Zhu, Y
Cao, W
liu, X
Liu, Z
liu, X
Tian, Y
Zhu, Y
Cao, W
Cao, Q
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
Han, M
Zhang, N
Armstrong, P
Topics
Precision Nutrient Management
Remote Sensing Applications in Precision Agriculture
Machine Vision / Multispectral & Hyperspectral Imaging Applications to Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Precision Nutrient Management
Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Remote Sensing Application / Sensor Technology
Applications of Unmanned Aerial Systems
Geospatial Data
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Site-Specific Nutrient, Lime and Seed Management
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Wireless Sensor Networks and Farm Connectivity
Type
Poster
Oral
Year
2012
2010
2014
2016
2008
2018
2022
2024
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Authors

Filter results23 paper(s) found.

1. Soil pH maps Derived from On-the-Go pH-Measurements as Basis for Variable Lime Application under German Conditions: Concept Development and Evaluation in Field Trials

... A. Borchert, D. Trautz, H. Olfs

2. Economic Evaluation of a Variable Lime Application Strategy Based on Soil pH Maps Derived from On-The-Go pH-Measurements under German Conditions

... A. Borchert, G. Recke, D. Dabbelt, D. Trautz, H. Olfs

3. Hyperspectral Imagery for the Detection of Nitrogen Stress in Potato for In-season Management

... T.J. Nigon, C. Rosen, D. Mulla, Y. Cohen, V. Alchanatis, R. Rud

4. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial Images

Potato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud

5. A Method for Combining Spatial and Hyperspectral Information for Delineation of Homogenous Management Zones

Hyperspectral (HS) remote sensing is a constantly developing field. New remote sensing applications of different fields constantly appear. The possibility of acquisition information about an object without physical contact is spanning new opportunities in many fields and for precision agricultural in particular. These opportunities demand constant improvement and development of new analysis approaches and algorithms,... Y. Cohen, V. Alchanatis, O. Levi, S. Cohen

6. 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

7. Spatial Mapping Of Penetrometer Resistance On Turfgrass Soils For Site-specific Cultivation

Site-specific management requires site-specific information.  Soil compaction at field capacity is a major stress on recreational turfgrass sites that requires frequent cultivation. Spatial mapping of penetrometer... K. Rice, T. Carson, J. Krum, I. Flitcroft, V. Cline, R. Carrow

8. Validation Of On-the-go Soil Ph-measurements – Primary Results From Germany

Until recently in-field variability for soil pH could not be considered for agronomic decisions (e.g. liming rates) because reliable spatial information was hardly available. The required density of soil pH-measurements could not be achieved by manual soil sampling due to time constraints and analysis costs for the vast number of samples. A comprehensive... H. Olfs, D. Trautz, A. Borchert

9. Spatial And Vertical Distribution Of Soil P, K, And Mg Content In A Vineyard Of The Do Ca Rioja Using Grid And Target Sampling Methods

  Knowledge of spatial variability of soil nutrient contents is very important to design a fertilization strategy based on the needs of the vine. Matching fertilization and nutritional plant needs is very important due to the influence of nutritional status of vineyards on productive and qualitative factors. The aim of this work was to study the spatial and vertical variability of P, K and Mg in a vineyard soil by two methods: (i) the grid sampling at three depth ranges (0-30,... O. Unamunzaga, A. Castell, G. Besga, R. Perez-parmo, A. Aizpurua

10. Automatic Detection And Mapping Of Irrigation System Failures Using Remotely Sensed Canopy Temperature And Image Processing

Today there is no systematic way to identify and locate failures of irrigation systems mainly because of the labor costs associated with locating the failures. The general aim of this study was to develop an airborne thermal imaging system for semi - automatic monitoring and mapping of irrigation system failures, specifically, of leaks and clogs. Initially, leaks and clogs were simulated by setting controlled trials in table grapes vineyards and olive groves. Airborne thermal... V. Alchanatis, Y. Cohen, M. Sprinstin, A. Cohen, I. Zipori, A. Dag, A. Naor

11. Are Thermal Images Adequate For Irrigation Management?

Thermal crop sensing technologies have potential as tools for monitoring and mapping crop water status, improving water use efficiency and precisely managing irrigation. As thermal sensors and imagers became more affordable, various platforms were examined to allow for canopy- and field-scale acquisitions of canopy temperature and to extract maps of water status variability. Various canopy temperature statistics and crop water stress index (CWSI) were used to estimate water status... O. Rosenberg, V. Alchanatis, Y. Saranga, A. Bosak, Y. Cohen

12. Positioning Strategy of Maize Hybrids Adjusting Plant Population by Management Zones

Choice of hybrid and accurate amount of plants per area determines grain yield and consequently net incomes. Local field adjustment in plant population is a strategy to manage spatial variability and optimize environmental resources that are not under farmer control (like soil type and water availability). This study aims to evaluate the response of hybrids by levels of plant population across management zones (MZ). Six different hybrids and five rates of plant populations were analyzed starting... A.A. Anselmi, J.P. Molin, M.T. Eitelwein, R. Trevisan, A. Colaço

13. Crop Water Stress Mapping for Site Specific Irrigation by Thermal Imagery and Artificial Reference Surfaces

Variable rate irrigation machines or solid set systems have become technically feasible; however, crop water status mapping is necessary as a blueprint to match irrigation quantities to site-specific crop water demands. Remote thermal sensing can provide these maps in sufficient detail and at a timely delivery. In a set of aerial and ground scans at the Hula Valley, Israel, digital crop water stress maps were generated using geo-referenced high- resolution thermal imagery and artificial reference... M. Meron, J. Tsipris, V. Orlov, V. Alchnatis, Y. Cohen

14. Using Unmanned Aerial Vehicle and Active-Optical Sensor to Monitor Growth Indices and Nitrogen Nutrition of Winter Wheat

Using unmanned aerial vehicle (UAV) remote sensing monitoring system can rapidly and cost-effectively provide crop canopy information for growth diagnosis and precision fertilizer regulation. RapidScan CS-45 (Holland, Lincoln, NE, USA) is a portable active-optical sensor designed for timely, non-destructive obtaining plant canopy information without being affected by weather condition. UAV equipped with RapidScan, is of great significant for rapidly monitoring crop growth and nitrogen (N) status.... X. Liu, Q. Cao, Y. Tian, Y. Zhu, Z. Zhang, W. Cao

15. Using Geospatial Data to Assess How Climate Change May Affect Land Suitability for Agriculture Production

Finding solutions to the challenge of sustainably feeding the world’s growing population is a pressing research need that cuts across many disciplines including using geospatial data. One possible area could be developing agricultural frontiers. Frontiers are defined as land that is currently not cultivated but that may become suitable for agriculture under climate change. Climate change may drive large-scale geographic shifts in agriculture, including expansion in cultivation at the thermal... K. Kc, L. Hannah, P. Roehrdanz, C. Donatti, E. Fraser, A. Berg, L. Saenz, T.M. Wright, R.J. Hijmans, M. Mulligan

16. The Use of Spatial and Temporal Measures to Enhance the Sensitivity of Satellite-based Spectral Vegetation Indices to (Water) Stress in Maize Fields

Climate change and water scarcity are reducing the available irrigation water for agriculture thus turning it into a limited resource. Today calculating and estimating crop water requirements are achieved through the ETc FAO-56 model where the effect of climate on crop water requirement is determined through the water evaporation from the soil and plant (ETref), and a calendar crop coefficient (Kc). Models that... Y. Goldwasser, V. Alchanati, E. Goldshtein, Y. Cohen, A. Gips, I. Nadav

17. Comparison of Canopy Extraction Methods from UAV Thermal Images for Temperature Mapping: a Case Study from a Peach Orchard

Canopy extraction using thermal images significantly affects temperature mapping and crop water status estimation. This study aimed to compare several canopy extraction methodologies by utilizing a large database of UAV thermal images from a precision irrigation trial in a peach orchard. Canopy extraction using thermal images can be attained by purely statistical analysis (S), a combination of statistical and spatial analyses (SS), or by synchronizing thermal and RGB images, following RGB statistical... L. Katz, A. Ben-gal, I. Litaor, A. Naor, A. Peeters, E. Goldshtein, V. Alchanatis, Y. Cohen

18. Effectiveness of Different Precision Soil Sampling Strategies for Site-Specific Nutrient Management in Row-Crops

Soil sampling is an important component of site-specific nutrient management in precision agriculture. While precision soil sampling strategies such as grid or zone have been around for a while, the adoption and utilization of these strategies varies considerably among the growers, especially in the southeastern United States. The selection of an appropriate grid size or management zone further differ among the users depending on several factors. In order to better understand how some of the commonly... M.W. Tucker, S. Virk, G. Harris, J. Lessl, M. Levi

19. Developing a Wheat Precision Nitrogen Management Strategy by Combining Satellite Remote Sensing Data and WheatGrow Model

Precision nitrogen (N) management (PNM) is becoming increasingly popular due to its ability to synchronize crop N demand with soil N supply spatiotemporally. The previous evidence has demonstrated that variable rate fertilization contributes to achieving high yields and high efficiencies. However, PNM at the regional level remains unclear and challenging. This study aims to develop a novel management zone (MZ)-based PNM strategy (MZ-PNM) to optimize the basal and topdressing N rates at the regional... Y. Miao, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao, X. Chen, Y. Li

20. Potential Benefits of Variable Rate Nitrogen Topdressing Strategy Coupled with Zoning Technique: a Case Study in a Town-scale Rice Production System

Integrating remote sensing (RS)-based variable rate nitrogen (N) recommendation (VRNR) algorithms and management zones (MZs) may improve the accuracy and efficiency of site-specific N management. However, its potential benefits for application in commercial rice production systems can hardly be assessed, since it requires to intervene in common agricultural practices and causes certain economic and environmental consequences. Through a machine learning approach, this study aims to comprehensively... J. Zhang, W. Wang, Z. Fu, Q. Cao, Y. Tian, Y. Zhu, W. Cao, X. Liu

21. Optimizing Nitrogen Application in Global Wheat Production by an Integrated Bayesian and Machine Learning Approach

Wheat production plays a pivotal role in global food security, with nitrogen fertilizer application serving as a critical factor. The precise application of nitrogen fertilizer is imperative to maximize wheat yield while avoiding environmental degradation and economic losses resulting from excess or inadequate usage. The integration of Bayesian and machine learning methodologies has gained prominence in the realm of agricultural research. Bayesian and machine learning based methods have great... Z. Liu, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao

22. 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

23. Optimizing the Connectivity of Wireless Underground Sensor Networks

In the rapidly evolving field of wireless communication, extending this technology into subterranean realms presents a frontier replete with unique challenges and opportunities. This study explores the intricate dynamics of establishing reliable connectivity in underground environments, a critical component for applications in diverse fields including precision agriculture and environmental monitoring. The distinct characteristics of underground settings impose significant obstacles for wireless... M. Han, N. Zhang, P. Armstrong