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Nogueira Gusmão, P.H
Nunes, D.N
Nogueira, F.I
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Authors
Camargo, S
da Silveira, E.M
Nogueira, F.I
Campos, A.F
Mércio, V.Z
Nunes, D.N
Oliveira, R.P
Corrêdo, L.D
Peternelli, L
Pedrosa, A.W
Galvan, V.H
Souza, J
Lopes de Brito Filho, A
Morlin Carneiro, F
Pereira Costa, G
Dos Santos Silva, B
Nogueira Gusmão, P.H
Pereira da Silva, R.P
Topics
Predictive Modeling, Phenotyping, and Digital Tools for Decision Support
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Oral
Poster
Year
2026
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Filter results3 paper(s) found.

1. Estimating Grape Bunch Yield Using Convolutional Neural Networks and Proximal RGB Imaging in the Brazilian Pampa

Viticulture of fine wines has become an increasingly important economic activity in the Pampa biome of southern Brazil, a relatively recent production frontier with approximately two decades of commercial development. In this emerging region, accurate prediction of grapewine productivity represents one of the most relevant challenges for growers, as reliable early estimates directly support decision-making related to harvest planning, logistics, labor allocation, and market... S. Camargo, E.M. Da Silveira, F.I. Nogueira, A.F. Campos, V.Z. Mércio

2. Management Zone Delineation for the Optimization of Nitrogen Use Efficiency in Arabica Coffee Crops

Precision coffee farming requires efficient methods for Nitrogen (N) management—an input of high cost and environmental impact, whose optimization faces challenges due to the topographical characteristics of regions such as the Zona da Mata in Minas Gerais, Brazil. This study evaluates and compares different dimensionality reduction models for agricultural management zone (MZ) delineation, aiming to maximize nitrogen fertilizer use efficiency in Arabica coffee plantations. A dataset comprising... D.N. Nunes, R.P. Oliveira, L.D. Corrêdo, L. Peternelli, A.W. Pedrosa, V.H. Galvan, J. Souza

3. Estimating Peanut Losses Using Machine Learning with Soil and Weather Data

Mechanized peanut harvesting is an important phase of the production system, directly affecting both production costs and crop yield. However, due to interactions among soil conditions, plant characteristics, and machine performance, this operation is carried out under challenging conditions that may result in high levels of loss. These losses are classified as visible when pods remain on the soil surface after digging and as invisible when they are incorporated into the soil profile, making them... A. Lopes De Brito Filho, F. Morlin Carneiro, G. Pereira Costa, B. Dos Santos Silva, P.H. Nogueira Gusmão, R.P. Pereira Da Silva