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Tahir, M
Laurent, P
Stočes, M
Shilo, T
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Authors
Tahir, M
Marine, L
Manon, M
Claire, G
Laurent, P
Mostafa, F
Zoran, C
Naima, B
Sébastien, D
Olivier, G
Jarolimek, J
Stočes, M
Ulman, M
Vaněk, J
Pelta, R
Beeri, O
Shilo, T
Tarshish, R
Beeri, O
Pelta, R
Sade, Z
Shilo, T
Topics
Sensor Application in Managing In-season Crop Variability
Precision Crop Protection
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Poster
Oral
Year
2012
2016
2022
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Filter results5 paper(s) found.

1. Model for Remote Estimation of Nitrogen Contents of Corn Leaf Using Hyper-Spectral Reflectance under Semi-Arid Condition.

Accuracy and precision of nitrogen estimation can be improved by hyperspectral remote sensing that leads... M. Tahir

2. Using Multiplex® to Manage Nitrogen Variability in Champagne Vineyard

... L. Marine, M. Manon, G. Claire, P. Laurent, F. Mostafa, C. Zoran, B. Naima, D. Sébastien, G. Olivier

3. Technology Support for Game Monitoring As a Tool for Damages Reduction of Field Crops

Wild boars (Sus scrofa) are increasingly becoming the main cause of field crops damage in Czech Republic and central Europe area. There are many reasons why wild boars population is growing. The major reason is most likely change in the composition of field crops. In some areas in particular there is focus on oilseed rape and maize, for which there are also recorded the biggest losses. One of the key discussion topics is the issue of estimation of animal quantities and its traceability.... J. Jarolimek, M. Stočes, M. Ulman, J. Vaněk

4. A Hyperlocal Machine Learning Approach to Estimate NDVI from SAR Images for Agricultural Fields

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture used globally since the 1970s. The NDVI is sensitive to the biochemical and physiological properties of the crop and is based on the Red (~650 nm) and NIR (~850 nm) spectral bands. It is used as a proxy to monitor crop growth, correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions which might alter... R. Pelta, O. Beeri, T. Shilo, R. Tarshish

5. Multi-sensor Imagery Fusion for Pixel-by-pixel Water Stress Mapping

Evaluating water stress in agricultural fields is fundamental in irrigation decision-making, especially mapping the in-field water stress variability as it allows real-time detection of system failures or avoiding yield loss in cases of unplanned water stress. Water stress mapping by remote sensing imagery is commonly associated with the thermal or the short-wave-infra-red (SWIR) bands. However, integration of multi-sensors imagery such as radar imagery or sensors with only visible and near-infra-red... O. Beeri, R. Pelta, Z. Sade, T. Shilo