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Deep Learning for Agriculture

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Biological and Agricultural Engineering Division Technical Session 1

Tagged Division

Biological and Agricultural Engineering

Page Count

13

DOI

10.18260/1-2--34371

Permanent URL

https://peer.asee.org/34371

Download Count

128

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Paper Authors

biography

Maria Pantoja California Polytechnic State University, San Luis Obispo

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Maria Pantoja
Computer Engineering
Computer Science & Software Engineering
Office: 14-211
Phone Number: 805-756-1330
Email: mpanto01@calpoly.edu
Homepage:
https://cpe.calpoly.edu/faculty/mpanto01/
Biography
B.S., Universidad Politecnica de Valencia, Spain
Ph.D., Santa Clara University

Research Interests
High Performance Computing
Neural-Electronics
Parallel Computing

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biography

Franz J. Kurfess California Polytechnic State University, San Luis Obispo Orcid 16x16 orcid.org/0000-0003-1418-7198

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Franz J. Kurfess is a professor in the Computer Science and Software Engineering Department, California Polytechnic State University, San Luis Obispo, where he teaches mostly courses in Artificial Intelligence, Human-Computer Interaction, and User-Centered Design. Before joining Cal Poly, he was with Concordia University in Montreal, Canada, the New Jersey Institute of Technology, the University of Ulm, Germany, the International Computer Science Institute in Berkeley, CA, and the Technical University in Munich, where he obtained his M.S. and Ph.D. in Computer Science.

His main areas of research are Artificial Intelligence and Human-Computer Interaction, with particular interest in the intersection of the two fields.

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Irene Humer California Polytechnic State University, San Luis Obispo Orcid 16x16 orcid.org/0000-0003-2647-4813

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Ph. D. Electrical Engineering and Information Technology, Vienna University of Technology
M. S. Physics, University of Vienna
M. S. Education Physics and Mathematics, University of Vienna

Research Interests: Computer Science Education, Physics Simulation, Applied Computing

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Abstract

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.

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

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