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Rodrigues, F
Rontani, F
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Authors
Souza, E
Schenatto, K
Rodrigues, F
Rocha, D
Bazzi, C.L
Barbosa, M
Duron, D
Rontani, F
Bortolon, G
Moreira, B
Oliveira, L
Setiyono, T
Shiratsuchi, L
Silva, R.P
Holland, K.H
Topics
Precision Conservation Management
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2014
2024
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1. 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

2. Multi-sensor Remote Sensing: an AI-driven Framework for Predicting Sugarcane Feedstock

Predicting saccharine and bioenergy feedstocks in sugarcane enables stakeholders to determine the precise time and location for harvesting a better product in the field. Consequently, it can streamline workflows while enhancing the cost-effectiveness of full-scale production. On one hand, Brix, Purity, and total reducing sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for industrial food and fuel processing. On the other hand, Cellulose, Hemicellulose,... M. Barbosa, D. Duron, F. Rontani, G. Bortolon, B. Moreira, L. Oliveira, T. Setiyono, L. Shiratsuchi, R.P. Silva, K.H. Holland