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Shilo, T
Molin, J.P
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Authors
Molin, J.P
Colaço, A.F
Colaço, A.F
Rosa, H.J
Molin, J.P
Saraiva, A.M
Santos, R.T
Molin, J.P
Pelta, R
Beeri, O
Shilo, T
Tarshish, R
Beeri, O
Pelta, R
Sade, Z
Shilo, T
Topics
Precision Horticulture
Information Management and Traceability
Food Security and Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Poster
Oral
Year
2012
2022
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1. Variable Rate Fertilization for Citrus

To improve economic and environmental sustainability new management strategies has been considered to citrus production. Especially on grain crops, Precision Agriculture (PA) has proved to be a successful tool to manage crop fields according to their variability, mainly through variable rate (VRT) fertilization practice. Although VRT technology is already being used on commercial citrus orchards, few academic researches have approached... J.P. Molin, A.F. Colaço

2. A Model to Analyze “As-Applied” Reports of Variable Rate Applications

Variable rate technology enables users to access crop inputs such as fertilizers and pesticides, based on site specific information. This technology combines a variable rate control system, positioning system and GIS software to enable variable rate application. During operation some of these systems report information (“as-applied” files) about target rates and actual applied rates on georeferenced points along the tracks.... A.F. Colaço, H.J. Rosa, J.P. Molin

3. Comparison of Algorithms for Delineating Management Zones

... A.M. Saraiva, R.T. Santos, J.P. Molin

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