Proceedings
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| Filter results6 paper(s) found. |
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1. Comparison Of Management Zones Generated By The K-Means And Fuzzy C-Means MethodsThe 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 |
2. The Influence Of The Interpolation Method In The Management Zones GenerationThe 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 |
3. Creating Thematic Maps and Management Zones for Agriculture FieldsThematic 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 |
4. AgDataBox-IoT - Managing IoT Data and Devices on Precision AgricultureThe 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 |
5. Spatial Data Interpolation in the AgDataBox Platform Using Graphics Processing Unit ParallelismPrecision agriculture platforms increasingly operate as integrated ecosystems that collect, store, and process large volumes of heterogeneous spatial data originating from multiple sources, including soil sampling, onboard sensors embedded in agricultural machinery, yield monitors, and remote sensing technologies such as satellites and unmanned aerial vehicles (UAVs). These platforms play a fundamental role in transforming raw georeferenced data into actionable information that supports site-specific... R. Sobjak, V.H. Malacarne, C.L. Bazzi, E. Souza, K. Schenatto, M. Rodrigues |
6. Automatic Creation of Thematic Maps and Management Zones Using Agdatabox-fast TrackPrecision agriculture encompasses the strategic application of inputs in requisite quantities at optimal times to enhance overall productivity. An essential aspect of this methodology is the formulation of thematic maps (TMs) and management zones (MZs). Despite their critical importance, delineating TMs and MZs requires substantial technical expertise in their construction, making their application challenging, particularly for smaller producers, due to the need for a specialized multidisciplinary... J. Aikes Junior, E. Souza, C.L. Bazzi, R. Sobjak, E. Souza |