Proceedings

Find matching any: Reset
Abdinoor, J.A
Alchanatis , V
Add filter to result:
Authors
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
Alchanatis, V
Cohen, Y
Sprinstin, M
Cohen, A
Zipori, I
Dag, A
Naor, A
Rosenberg, O
Alchanatis, V
Saranga, Y
Bosak, A
Cohen, Y
Gan, H
Lee, W
Alchanatis, V
Gan, H
Lee, W.S
Alchanatis, V
Abd-Elrahman, A
Katz, L
Ben-Gal, A
Litaor, I
Naor, A
Peeters, A
Goldshtein, E
Alchanatis, V
Cohen, Y
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
Zsebő, S
Kukorelli, G
Vona, V
Bede, L
Stencinger, D
Kovacs, A
Milics, G
Kulmany, I.M
Horváth, B
Hegedűs, G
Abdinoor, J.A
Kulmany, I.M
Horváth, B
Kukorelli, G
Zsebő, S
Stencinger, D
Borbás, Z
Pecze, R
Bede, L
Varga, Z
Kósa, A
Pinke, G
Hashim, Z.K
Hegedűs, G
Abdinoor, J.A
Agampodi, G.S
Topics
Remote Sensing Applications in Precision Agriculture
Machine Vision / Multispectral & Hyperspectral Imaging Applications to Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Scouting and Field Data collection with Unmanned Aerial Systems
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
2024
Home » Authors » Results

Authors

Filter results13 paper(s) found.

1. 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

2. 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

3. 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

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. 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

6. 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

7. A Photogrammetry-based Image Registration Method for Multi-camera Systems

In precision agriculture, yield maps are important for farmers to make plans. Farmers will have a better management of the farm if early yield map can be created. In Florida, citrus is a very important agricultural product. To predict citrus production, fruit detection method has to be developed. Ideally, the earlier the prediction can be done the better management plan can be made. Thus, fruit detection before their mature stage is expected. This study aims to develop a thermal-visible camera... H. Gan, W. Lee, V. Alchanatis

8. An Active Thermography Method for Immature Citrus Fruit Detection

Fast and accurate methods of immature citrus fruit detection are critical to building early yield mapping systems. Previously, machine vision methods based on color images were used in many studies for citrus fruit detection. Despite the high resolutions of most color images, problems such as the color similarity between fruit and leaves, and various illumination conditions prevented those studies from achieving high accuracies. This project explored a novel method for immature citrus fruit detection... H. Gan, W.S. Lee, V. Alchanatis, A. Abd-elrahman

9. 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

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

12. Comparison of NDVI Values at Different Phenological Stages of Winter Wheat (Triticum Aestivum L.)

The main objective of this study is to monitor, detect and quantify the presence of live green vegetation with the MicaSense RedEdge-MX Dual Camera System (MS) mounted on a DJI Matrice 210 V2 and GreenSeeker HCS 250 (GS) in winter wheat (Triticum aestivum L.) by using Normalized Difference Vegetation Index (NDVI). Surveys were conducted in the North-Western part of Hungary, in Mosonmagyaróvár on six different dates. A small-scale field trial in winter wheat was constructed as a randomized... S. Zsebő, G. Kukorelli, V. Vona, L. Bede, D. Stencinger, A. Kovacs, G. Milics, I.M. Kulmany, B. Horváth, G. Hegedűs, J.A. Abdinoor

13. Evaluation of the Effect of Different Herbicide Treatments by Using UAV in Maise (Zea mays L.) Cultivation – First Experiences in a Long-term Experiment at Széchenyi István University, Hungary

As part of the Green Deal, the European Union has set a goal to reduce the use of chemical pesticides by 50 percent until 2030. To achieve this goal, in addition to reducing the amount of pesticide used, attention must also be paid to monitoring the temporal and spatial effects of pesticides on weeds during the cultivation of various crops. Hence, Syngenta Ltd., collaborating with researchers, aimed to monitor the effect of five different types of herbicides by UAV in two tillage treatments (CN... I.M. Kulmany, B. Horváth, G. Kukorelli, S. Zsebő, D. Stencinger, Z. Borbás, R. Pecze, L. Bede, Z. Varga, A. Kósa, G. Pinke, Z.K. Hashim, G. Hegedűs, J.A. Abdinoor, G.S. Agampodi