Pantoja, M., & Kurfess, F. J., & Humer, I. (2020, June), Deep Learning for Agriculture  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34371

\bibitem{asee_peer_34371}
  Pantoja, M., \& Kurfess, F. J., \& Humer, I. (2020, June), \emph{Deep Learning for Agriculture}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--34371

Maria Pantoja, Franz J. Kurfess, and Irene Humer.  "Deep Learning for Agriculture".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/34371 Internet.  21 Dec, 2024

\bibitem{asee_peer_34371}
  Maria Pantoja, Franz J. Kurfess, and Irene Humer.
  "Deep Learning for Agriculture".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/34371 Internet.  21 Dec, 2024

@INPROCEEDINGS{asee_peer_34371
author = "Maria Pantoja, Franz J. Kurfess, and Irene Humer"
title = "Deep Learning for Agriculture"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/34371}
number = {10.18260/1-2--34371}
}

TY  - CPAPER
AB  - The global population is estimated to reach 8 billion by 2023. To feed such an
immense population in a sustainable way, while also enabling farmers to make a living, requires
the modernization of production methods in agriculture. In recent years there has been a lot of
excitement in academic research and industry about the application of modern computer
technology to farming, making farming one of the favorites for investors. According to Forbes
Magazine, the agricultural technology gold rush began in 2013, with Monsanto’s purchase of the
agricultural data company, The Climate Corporation, for $930 million. The total investment
in 2017 topped $1.5 billion, setting a new record. All economic indicators point to a huge
increase in technology and in particular software used in the agriculture fields.
The need for advanced technology in agriculture is clear. The technology is being developed and
ready, but what is still lacking is the number of professionals that have both skills - agriculture
and technological knowledge - in particular in advanced computing methods like computer
vision and deep learning.
The main goal of this paper is to report on our approach to close the gap between domain experts
in agriculture and computer scientists by developing a practical, hands-on activity in the form of
a workshop or tutorial specifically targeted at agricultural engineers and practitioners interested
in applying computer vision techniques to solve agricultural problems. The tutorial consists of
specific examples like detecting and counting bees, segmentation of fruit trees and automatic
fruit classification. The examples for the tutorials are chosen because of their simplicity of
implementation and because they are also easily expandable into more complex projects. For
example, the segmentation tutorial can be used to estimate pruning weight which is useful to
determine the baseline vigor levels of the fruit trees. It is one of the best ways to quantify areas
of the orchards that are not uniform in terms of expected yield, which will enable the use of
precision agriculture. The benefits of precision agriculture are multifold leading to reductions in
cost and increases in production by targeting specific areas. We explain and develop the tutorials
for agricultural engineers assuming no previous knowledge in computer programming or
computer vision.
AU  - Maria Pantoja
AU  - Franz J. Kurfess
AU  - Irene Humer
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - Deep Learning for Agriculture
UR  - https://peer.asee.org/34371
DO  - 10.18260/1-2--34371
ER  -