Proceedings
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| Filter results7 paper(s) found. |
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1. Predictive Analysis of Fertilizer Efficiency with Machine LearningFertilizers play a key role in agribusiness, both as an essential input for agricultural productivity and as a strategic component in the commercial chain. They provide nutrients that are indispensable for soil correction and crop growth, such as nitrogen, phosphorus, and potassium, allowing the soil to maintain its capacity to sustain crops even after several harvests. It is estimated that about 50% of global food production depends on the use of fertilizers, and in Brazil, these inputs represent... C.S. Santos, R.K. Weber |
2. Data Governance Platform for Precision Agriculture: Enhancing Traceability and SustainabilityPrecision Agriculture (PA) is one of the enablers of data-driven agriculture. Digital Agriculture (DA) tools are increasingly vital in driving the adoption of PA techniques across small, medium, and large-scale farming operations. These technologies, including the Internet of Things (IoT), sensors, drones, satellite imagery, Artificial Intelligence (AI), and Big Data, work synergistically to capture detailed information on soil conditions, plant health, climate, and machinery performance. This... E.A. Speranza, R.Y. Inamasu, L.A. Romani, J. Naime, R. Sobjak, I. Vacari, C.L. Bazzi, S. Shibusawa |
3. Delineation of Management Zones for the Adoption of Precision and Digital Agriculture in Steep-Sloped Arabica Coffee Production AreasCoffea arabica production in Brazil, particularly in regions of São Paulo and Minas Gerais, occurs in environments with a high diversity of climates, altitudes, and soils. The municipality of Caconde (SP) stands out with approximately 11,000 hectares of coffee, predominantly on small properties with altitudes above 800 meters and steep slopes. These characteristics are conducive to the production of high-quality, value-added coffees. Optimizing the use of natural resources and agricultural... E.A. Speranza, C.R. Grego, T. Santos, G.C. Rodrigues, R.Y. Inamasu |
4. Spatiotemporal Variability of Apple Tree Vegetative Vigor Using Proximal SensingThe largest apple production in Brazil is located in the southern region of the country, which has a subtropical climate, borderline conditions for the production of a fruit native to temperate climates. Thus, excessive vegetative growth frequently occurs, negatively impacting productivity and quality in the orchard. This leads to the diversion of productive resources to ancillary activities, such as the application of growth regulators and green pruning, negatively affecting the producer. Currently,... L. Gebler, A. De Rossi, J.T. De Abreu, E.A. Speranza, A. Sessi, L.D. Marchioretto |
5. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in AgricultureSatellite remote sensing has become a key data source for precision agriculture, particularly for monitoring vegetation dynamics and soil water stress over large areas. Multispectral satellite imagery enables the computation of vegetation indices, including NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which are commonly employed to quantify vegetation health, vigor, and canopy development. However, the practical use of satellite imagery in data-driven agricultural... A. Heideker, E.A. Speranza, E. Ferreira, D. Silva, C. Kamienski, R. Bianchi |
6. Combining Orbital and Proximal Sensing to Map Management Zones in Precision Viticulture: A Spatiotemporal AnalysisPrecision agriculture stands out by mapping the spatiotemporal variability of vineyards to understand the interdependence between causes and effects throughout production cycles. Vegetative vigor, which can be estimated using vegetation indices calculated from proximal and orbital sensors, is a fundamental parameter for indicating this variability and assisting in the definition of potential, time-constant management zones. The objective of this study was to evaluate and compare the use of proximal... E.A. Speranza, J.R. Da Silva, L.R. Correa, L.H. Bassoi |
7. A Statistical Approach to Defining Coffee Management Zones: Integrating Apparent Soil Electrical Conductivity, Altimetry and Satelitte Indices for Moisture MonitoringCharacterizing the spatial and temporal behavior of soil and plant attributes represents the elementary step toward adoption precision agriculture. The expanding availability of multi-temporal remote sensing imagery with enhanced spatial resolution has rendered the delineation of management zones (MZ) an increasingly feasible strategy, especially when the intention is to carry out spatially differentiated interventions considering the vegetative vigor throughout the crop cycle or the plant yield.... E.A. Speranza, E.J. Ferreira, L.H. Bassoi, L.M. Rabello, C.M. Vaz, A. Torre-neto |