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Randriamanga, D
Ridout, M
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Authors
Keresztes, B
Da Costa, J
Randriamanga, D
Germain, C
Abdelghafour, F
Cook, S
Lacoste, M
Evans, F
Ridout, M
Gibberd, M
Oberthur, T
Topics
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
On Farm Experimentation with Site-Specific Technologies
Type
Oral
Year
2018
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1. Real-Time Fruit Detection Using Deep Neural Networks

Proximal imaging using tractor-mounted cameras is a simple and cost-effective method to acquire large quantities of data in orchards and vineyards. It can be used for the monitoring of vegetation and for the management of field operations such as the guidance of smart spraying systems for instance. One of the most prolific research subjects in arboriculture is fruit detection during the growing season. Estimations of fruit-load can be used for early yield assessments and for the monitoring of... B. Keresztes, J. Da costa, D. Randriamanga, C. Germain, F. Abdelghafour

2. An On-farm Experimental Philosophy for Farmer-centric Digital Innovation

In this paper, we review learnings gained from early On-Farm Experiments (OFE) conducted in the broadacre Australian grain industry from the 1990s to the present day. Although the initiative was originally centered around the possibilities of new data and analytics in precision agriculture, we discovered that OFEs could represent a platform for engaging farmers around digital technologies and innovation. Insight from interacting closely with farmers and advisors leads us to argue for a change... S. Cook, M. Lacoste, F. Evans, M. Ridout, M. Gibberd, T. Oberthur