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Shapira , U
Todman, L
Bölenius, E
Souza, E
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Authors
Bonfil, D.J
Shapira, U
Karnieli, A
Herrmann, I
Kinast, S
Shapira , U
Herrmann, I
Karnieli, A
Bonfil, D.J
Bölenius, E
Arvidsson, J
Souza, E
Schenatto, K
Rodrigues, F
Rocha, D
Bazzi, C.L
Schenatto, K
Bazzi, C.L
Bier, V
Souza, E
Souza, E
Schenatto, K
Bazzi, C
Karampoiki, M
Todman, L
Mahmood, S
Murdoch, A
Paraforos, D
Hammond, J
Ranieri, E
Bazzi, C.L
Oliveira, W.K
Sobjak, R
Schenatto, K
Souza, E
Hachisuca, A
Franz, F
Topics
Remote Sensing Applications in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Precision Conservation Management
Education and Outreach in Precision Agriculture
Big Data, Data Mining and Deep Learning
Wireless Sensor Networks and Farm Connectivity
Type
Poster
Oral
Year
2012
2010
2014
2018
2022
2024
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Filter results8 paper(s) found.

1. 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

2. 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

3. Penetration Resistance And Yield Variation At Field Scale

In order to better explain spatial variations within fields, soil physical properties need to be studied in more depth. Relationships between soil physical parameters and yield, especially in the subsoil, are seldom studied since the characterization of soil variability at field or subfield scale using conventional methods is a labor intensive, very expensive, and time-consuming procedure, particularly when high-resolution data is required. However, soil physical properties... E. Bölenius, J. Arvidsson

4. Comparison Of Management Zones Generated By The K-Means And Fuzzy C-Means Methods

The generation of Management Zones (MZ) is an economic alternative to make viable the precision agriculture (RODRIGUES & ZIMBACK, 2002) because they work as operation units for the inputs localized application and as soil and culture sample indicators. For the field division in... E. Souza, K. Schenatto, F. Rodrigues, D. Rocha, C. Bazzi

5. The Influence Of The Interpolation Method In The Management Zones Generation

The definition of management zones (MZ) allows the concepts of precision agriculture (PA) to be used even in small producers. Methods for defining these MZ were created and are being used, obtaining satisfactory results with different crops and parameters (FLEMING & WESTFALL, 2000; ORTEGA & SANTIBÁÑEZ, 2007; MILANI et al., 2006). Through methodologies, the attributes that are influencing the productivity are selected and thematic maps are generated with the... K. Schenatto, C. Bazzi, V. Bier, E. Souza

6. Creating Thematic Maps and Management Zones for Agriculture Fields

Thematic maps (TMs) are maps that represent not only the land but also a topic associated with it, and they aim to inform through graphic symbols where a specific geographical phenomenon occurs. Development of TMs is linked to data collection, analysis, interpretation, and representation of the information on a map, facilitating the identification of similarities, and enabling the visualization of spatial correlations. Important issues associated with the creation of TMs are: selection of the... E. Souza, K. Schenatto, C. Bazzi

7. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri

8. AgDataBox-IoT - Managing IoT Data and Devices on Precision Agriculture

The increasing global population has resulted in a substantial demand for nourishment, which has prompted the agricultural sector to investigate ways to improve efficiency. Precision agriculture (PA) uses advanced technologies such as the Internet of Things (IoT) and sensor networks to collect and analyze field information. Although the advantages are numerous, the available data storage, management, and analysis resources are limited. Therefore, creating and providing a user-friendly web application... C.L. Bazzi, W.K. Oliveira, R. Sobjak, K. Schenatto, E. Souza, A. Hachisuca, F. Franz