Proceedings :: International Society of Precision Agriculture

Proceedings

Find matching any: Reset

Ngo, V.M

Inamassu, R.Y

» Add more authors

Add filter to result:

Authors

de Oliveira, R.P

Bernardi, A.C

Benites, V.D

Rabello, L.M

Inamassu, R.Y

Ngo, V.M

Le-Khac, N

Kechadi, M

Topics

Spatial Variability in Crop, Soil and Natural Resources

Big Data, Data Mining and Deep Learning

Type

Poster

Oral

Year

2012

2018

Home » Authors » Results

Authors

Filter results2 paper(s) found.
1. Spatial Variability Index Based On Soil Properties for Notill and Pasture Site-Specific Management in Brazil. Quantitative characterization of soil properties spatial variation has first been applied... R.P. De oliveira, A.C. Bernardi, V.D. Benites, L.M. Rabello, R.Y. Inamassu Download paper
2. An Efficient Data Warehouse for Crop Yield Prediction Nowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-processed... V.M. Ngo, N. Le-khac, M. Kechadi Download paper

Filter results2 paper(s) found.

1. Spatial Variability Index Based On Soil Properties for Notill and Pasture Site-Specific Management in Brazil.

Quantitative characterization of soil properties spatial variation has first been applied... R.P. De oliveira, A.C. Bernardi, V.D. Benites, L.M. Rabello, R.Y. Inamassu

Download paper

2. An Efficient Data Warehouse for Crop Yield Prediction

Nowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-processed... V.M. Ngo, N. Le-khac, M. Kechadi

Download paper