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 Author(s): VASQUES, G. M.; RODRIGUES, H.; OLIVEIRA, R. P. de; CEDDIA, M. B.; HERNANI, L. C.; TAVARES, S. R. de L. The objective was to integrate soil data from two proximal soil sensors to improve the delineation of homogeneous soil regions using a k-means clustering algorithm defining three distinct management z... ... |
 Author(s): VASQUES, G. de M.; RODRIGUES, H. M.; COELHO, M. R.; BACA, J. F. M.; DART, R. de O.; OLIVEIRA, R. P. de; TEIXEIRA, W. G.; CEDDIA, M. B. Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and c... ... |
 Author(s): VASQUES, G. de M.; RODRIGUES, H. M.; OLIVEIRA, R. P. de; HERNANI, L. C.; TAVARES, S. R. de L. The objectives are to delineate soil management zones from soil proximal sensor data, and compare soil property values among zones in a 72-ha crop field in southeastern Brazil. Apparent electrical con... ... |
 Author(s): RODRIGUES, H. M.; CEDDIA, M. B.; VASQUES, G. M.; MULDER, V. L.; HEUVELINK, G. B. M.; OLIVEIRA, R. P. de; BRANDAO, Z. N.; MORAIS, J. P. S.; NEVES, M. L.; TAVARES, S. R. de L. The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number o... ... |
 Author(s): DEMATTÊ, J. A. M.; TERRA, F. da S.; OTTO, R.; TOMA, R. S.; PEREIRA, L. H.; NASCIMENTO, A. F. do; BORTOLETTO, M. A. M. When the harvesting of sugarcane involves a mechanized process, plant residues remain on the soil surface, which makes proximal and remote sensing difficult to monitor. This study aimed to evaluate, u... ... |
 Author(s): VASQUES, G. de M.; RODRIGUES, H. M.; TAVARES, S. R. de L.; HERNANI, L. C.; OLIVEIRA, R. P. de An optimized sampling design to assess soil property variation across the field and within management zones is proposed and validated in a 72-ha crop field in southeastern Brazil. An optimized sample... ... |
 Author(s): OLIVEIRA, R. P. de; RODRIGUES, H. M.; VASQUES, G. de M.; TAVARES, S. R. de L.; HERNANI, L. C.; BACA, J. F. M.; COELHO, M. R. Sustainable management of agricultural lands requires detailed information on soil properties. Although the literature has shown the potential of PSS data integration to predict spatial variations of... ... |
 Author(s): OSCO, L. P.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; MORIYA, E. A. S.; IMAI, N. N.; ESTRABIS, N.; IANCZYK, F.; ARAÚJO, F. F.; LIESENBERG, V.; JORGE, L. A. de C.; LI, J.; MA, L.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; CRESTE, J. E. This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient con... ... |
 Author(s): HERRMANN JUNIOR, P. S. de P.; RABELLO, L. M.; BERTUCCI NETO, V.; CRUVINEL, P. E. Agricultural sensors are crucial in improving crop yields and reducing resource waste. It has led researchers to develop affordable sensors that can be easily integrated into existing farming systems.... ... |
 Author(s): LEMOS, S. G.; NOGUEIRA, A. R. de A.; TORRE NETO, A.; PARRA, A.; ALONSO, J. An agrarian sensorial system based on temperature, moisture, and all solid-state ion-selective potentiometric sensors was developed with the objective of monitoring the behavior of H+ and Ca2+ ions in... ... |
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