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Title: Intelligent Agricultural Machinery Using Deep Learning
Author: Thomas, Gabriel
Balocco, Simone
Mann, Danny
Simundsson, Avery
Khorasani, Nioosha
Keywords: Aprenentatge automàtic
Intel·ligència artificial
Xarxes neuronals (Informàtica)
Maquinària agrícola
Machine learning
Artificial intelligence
Neural networks (Computer science)
Agricultural machinery
Issue Date: 12-Apr-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract: Artificial intelligence, deep learning, big data, self-driving cars, these are words that have become familiar to most people and have captured the imagination of the public and have brought hopes as well as fears. We have been told that artificial intelligence will be a major part of our lives, and almost all of us witness this when decisions made by algorithms show us commercial advertisements that specifically target our interests while using the web. In this paper, the conversation around artificial intelligence focuses on a particular application, agricultural machinery, but offers enough content so that the reader can have a very good idea on how to consider this technology for not only other agricultural applications such as sorting and grading produce, but also other areas in which this technology can be a part of a system that includes sensors, hardware and software that can make accurate decisions. Narrowing the application and also focusing on one specific artificial intelligence approach, that of deep learning, allow us to illustrate from start to end the steps that are usually considered and elaborate on recent developments on artificial intelligence.
Note: Versió postprint del document publicat a:
It is part of: IEEE Instrumentation & Measurement Magazine, 2021, vol. 24, num. 2, p. 93-100
Related resource:
ISSN: 1094-6969
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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