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Intelligent Agricultural Machinery Using Deep Learning
IEEE Instrumentation & Measurement Magazine ( IF 1.6 ) Pub Date : 2021-04-12 , DOI: 10.1109/mim.2021.9400957
Gabriel Thomas , Simone Balocco , Danny Mann , Avery Simundsson , Nioosha Khorasani

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.

中文翻译:


利用深度学习的智能农业机械



人工智能、深度学习、大数据、自动驾驶汽车,这些词已经为大多数人所熟悉,也激发了公众的想象力,带来了希望,也带来了恐惧。我们被告知人工智能将成为我们生活的重要组成部分,当算法做出的决策向我们展示专门针对我们在使用网络时的兴趣的商业广告时,几乎所有人都见证了这一点。在本文中,围绕人工智能的讨论重点关注农业机械这一特定应用,但提供了足够的内容,以便读者可以很好地了解如何考虑将该技术应用于其他农业应用,例如农产品分拣和分级以及其他领域,该技术可以成为包括传感器、硬件和软件在内的系统的一部分,可以做出准确的决策。缩小应用范围并关注一种特定的人工智能方法,即深度学习,使我们能够从头到尾说明通常考虑的步骤并详细说明人工智能的最新发展。
更新日期:2021-04-12
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