Proceedings
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| Filter results7 paper(s) found. |
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1. Machine Learning–driven Insights into Nitrogen Dynamics and Greenhouse Gas Emissions in Potato Production SystemsNitrogen (N) is an essential nutrient for potato vegetative growth, yet it remains one of the most challenging elements to manage in modern agricultural systems. Despite its critical role in crop productivity, excessive or poorly timed N application can lead to significant environmental losses, particularly through groundwater nitrate (NO3-N) leaching and emissions of nitrous oxide (N2O), a greenhouse gas approximately 300 times more potent than CO2. Therefore,... B. Javed, A. Cambouris, E. Smith, S. Dandrifosse, N. Ziadi, A. Karam |
2. Variable Seeding Rate to Manage Within-field VariabilityWithin-field variability can strongly influence final crop yield and the efficiency of agricultural inputs such as seeds, fertilizer, water, and agrochemicals, thus managing spatial variability through precision agriculture to optimize input use and improve sustainability can yield significant gains, provided that the mechanisms driving field variability are understood. Despite extensive research on the relationship between seeding density and yield, relatively little attention has been given... B. Maestrini, L.P. Pott, D. Bamberg, T. Liska, T. Rosado, L. Sander, T. Ruiz Moreno, N. Garcia Dutrez, F. Doeler, T. Van Der Wal, V. Kaster Marini, T. Amado, Nieuwenhuizen |
3. Scalable Offline Infrastructure for Agriculture 4.0: A Linux-Gateway Architecture for Edge Connectivity and Data SovereigntyBrazilian agribusiness has consolidated itself as an economic pillar, representing approximately 25% of the national GDP. However, the full rise of Agriculture 4.0 - characterized by the integration of IoT, Artificial Intelligence, and smart sensors -faces a scenario of profound technological inequality. Recent data indicate a disparate adoption of digital tools: while large-scale farms possess the financial capacity for complex infrastructures, small and medium-sized producers face connectivity... M. Andrade, C. Soares De Souza |
4. Integrated Evaluation of Precision Spraying Systems in Pecan Orchards: Smart Apply System™, Conventional Airblast, and Drone ApplicationPecan production in Georgia faces increasing challenges related to weather variability, rising labor costs, and higher prices of agricultural inputs, particularly fungicides required for scab (Venturia effusa) management. Conventional air-assisted sprayers operate at fixed application rates and do not account for tree size variability or canopy gaps, often resulting in overapplication and reduced efficiency. Emerging precision technologies, such as Smart Apply™ sprayers and spray... R.P. Oliveira, J.P. Silva, J.V. Martins, R. Dos Santos, M. Barbosa, L. Oliveira |
5. Topographic Modeling Using Remotely Piloted Aircraft to Identify Areas with Water Erosion Potential and to Plan Sowing LinesWater erosion constitutes one of the main factors of agricultural soil degradation. In this context, knowledge of the topography of agricultural fields and the planning of sowing lines guided by geotechnologies emerges as a strategy to mitigate surface runoff and soil loss. This study aimed to perform the topographic modeling of an agricultural field and to analyze the effect of using different sowing line designs on the longitudinal slope of these lines. The study was conducted in an agricultural... L. Silveira Pavão, A. Müllich, E. Rolim Farias Da Silva, R. Cavalcanti, M. Silveira De Farias, I. , J. Sgarbossa, L. |
6. Spatial Delineation of Site-Specific Management Units Using Vegetation Indices in Precision AgriculturePrecision Agriculture has incorporated Remote Sensing as an essential tool for characterizing the spatial variability of agricultural crops. Among the available spectral indices, vegetation indices stand out for their ability to represent vegetative vigor and spatial patterns associated with crop performance. This study aimed to evaluate the spatial stability of spectral indices obtained from a median composite for management zone delineation and to analyze their agreement with a yield map in... L.G. Kern, L. Silveira Pavão, . Müllich, I. Maldaner, L. , J. Sgarbossa, G. Kaefer Seganfredo, E. Rolim Farias Da Silva, M. Silveira Farias |
7. Relationship Between Temporal Variability of Soybean Yield and Stable Soil AttributesManagement zones are widely used in precision agriculture and can be defined by different factors; however, uncertainties remain regarding their temporal stability when based on a single soil attribute. This study aimed to analyze the relationship between a temporal series of yield from five agricultural fields and four stable soil attributes—clay content, soil organic matter (SOM), Topographic Wetness Index (TWI), and apparent electrical conductivity (ECa)—using multiple linear regression... G. Kaefer Seganfredo, L.G. Kern, L. Silveira Pavão, A. Müllich, I. Maldaner, J. Sgarbossa, L. , E. Rolim Farias Da Silva, M.S. Farias |