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VERSION:2.0
PRODID:IEEE vTools.Events//EN
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TZID:America/Santiago
BEGIN:DAYLIGHT
DTSTART:20220911T010000
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BEGIN:VEVENT
DTSTAMP:20221103T140704Z
UID:1AD4B333-7C86-4225-A50E-F7BB514DF39F
DTSTART;TZID=America/Santiago:20221027T140000
DTEND;TZID=America/Santiago:20221027T150000
DESCRIPTION:In recent years\, there has been a great interest in applicatio
 ns of machine learning in agricultural environments. However\, for many ag
 ricultural machine learning problems\, training datasets are site-specific
  (e.g.\, light condition\, time of the day\, one time of the season)\, mak
 ing it not trivial to obtain a model that can generalize abroad crop type\
 , cultivar\, management\, season\, among others. In addition\, the data-la
 belling process can always be labor and cost intensive\, especially with L
 iDAR data due to the variability of the crops and the sparse nature of the
  point cloud information. This study presents an open-source simulation to
 olbox that allow an easy generation of synthetic labelled data for RGB and
  point cloud information for different type of cultivars\, and how to use 
 that data for enabling a more efficient training in ML applications.\n\nCo
 -sponsored by: Facultad de Ingeniería\, Universidad Andrés Bello\n\nSpea
 ker(s): Dr. Dario Guevara\, \n\nVirtual: https://events.vtools.ieee.org/m/
 328479
LOCATION:Virtual: https://events.vtools.ieee.org/m/328479
ORGANIZER:ras.centro@ieeechile.cl
SEQUENCE:3
SUMMARY:Ciclo mensual de charlas robóticas
URL;VALUE=URI:https://events.vtools.ieee.org/m/328479
X-ALT-DESC:Description: <br /><p>In recent years\, there has been a great i
 nterest in applications of machine learning in agricultural environments. 
 However\, for many agricultural machine learning problems\, training datas
 ets are site-specific (e.g.\, light condition\, time of the day\, one time
  of the season)\, making it not trivial to obtain a model that can general
 ize abroad crop type\, cultivar\, management\, season\, among others. In a
 ddition\, the data-labelling process can always be labor and cost intensiv
 e\, especially with LiDAR data due to the variability of the crops and the
  sparse nature of the point cloud information. This study presents an open
 -source simulation toolbox that allow an easy generation of synthetic labe
 lled data for RGB and point cloud information for different type of cultiv
 ars\, and how to use that data for enabling a more efficient training in M
 L applications.</p>
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