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Chiang, R.C
Gebert, F.H
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Authors
Schwalbert, R
Carneiro Amado, T.J
Horbe, T
Corassa, G.M
Gebert, F.H
Kantipudi, K
Lai, C
Min, C
Chiang, R.C
Topics
Precision Nutrient Management
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2016
2018
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Filter results2 paper(s) found.

1. Adjustment of Corn Population and Nitrogen Fertilization Based on Management Zones

The main objective of this study was to adjust the corn population and nitrogen fertilization according to management zones, based on past grain yield maps (seven of soybean and three of corn) and soil electrical conductivity. The study was carried out in Não-Me-Toque, Rio Grande do Sul, Brazil, and it was conducted in a factorial strip blocks with 3 repetitions in each management zone, being the treatments: corn populations (56000, 64000, 72000, 80000 and 88000 plants ha-1),... R. Schwalbert, T.J. Carneiro amado, T. Horbe, G.M. Corassa, F.H. Gebert

2. Weed Detection Among Crops by Convolutional Neural Networks with Sliding Windows

One of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neural... K. Kantipudi, C. Lai, C. Min, R.C. Chiang