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Morandi, B
McBeath, T
Marine, L
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Authors
Marine, L
Manon, M
Claire, G
Laurent, P
Mostafa, F
Zoran, C
Naima, B
Sébastien, D
Olivier, G
Jones, B
McBeath, T
Wilhelm, N
Bresilla, K
Manfrini, L
Boini, A
Perulli, G
Morandi, B
Grappadelli, L.C
Topics
Sensor Application in Managing In-season Crop Variability
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2016
2018
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Filter results3 paper(s) found.

1. Using Multiplex® to Manage Nitrogen Variability in Champagne Vineyard

... L. Marine, M. Manon, G. Claire, P. Laurent, F. Mostafa, C. Zoran, B. Naima, D. Sébastien, G. Olivier

2. Shifting Fertiliser Response Zones in a Four Year, Whole-paddock Cereal Cropping Experiment.

Precision agriculture in cropping areas of dryland Australia has focused on managing within production zones. These are ideally stable, possibly soil- and topography-based areas within fields. There are many different ideas on how to delimit and implement zones, and a four year whole-field experiment, with low, medium and high treatment philosophies applied per 9m seeder/harvester width across the entire field, was established to explore how zones might best be established and used. The treatment... B. Jones, T. Mcbeath, N. Wilhelm

3. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the Tree

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli