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Laor, Y
Le-Khac, N
Schwalbert, R.A
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Authors
Corassa, G.M
Amado, T.J
Schwalbert, R.A
Reimche, G.B
Dalla Nora, D
Horbe, T.
Tabaldi, F.M
Ngo, V.M
Le-Khac, N
Kechadi, M
Rozenstein, O
Cohen, Y
Alchanatis , V
Behrendt, K
Bonfil, D.J
Eshel, G
Harari, A
Harris, W.E
Klapp, I
Laor, Y
Linker, R
Paz-Kagan, T
Peets, S
Rutter, M.S
Salzer, Y
Lowenberg-DeBoer, J
Topics
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Type
Poster
Oral
Year
2016
2018
2024
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1. High-resolution Mapping with On-the-go Soil Sensor and Its Relation with Corn Yield and Soil Acidity in a Dystrophic Red Oxisol

Spatial representations of soil attributes with low resolution can lead to gross errors of recommendation and compromise the efficiency of soil corrections and consequently the grain yield. However, obtaining the spatial variability of soil attributes with high resolution by soil sampling is not recommended because of its large time spent and high cost of laboratory analysis what makes difficult their large-scale application. This way, the on-the-go soil sensing has been used in precision agriculture... G.M. Corassa, T.J. Amado, R.A. Schwalbert, G.B. reimche, D. Dalla nora, T. . horbe, F.M. tabaldi

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

3. Data-driven Agriculture and Sustainable Farming: Friends or Foes?

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive.  It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience.  Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer