人工智能和深度学习在农作物收获机器人中的应用综述

T. U. Sane, Tanuj Sane
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引用次数: 1

摘要

随着人口的不断增长,对优质食品的需求也在增加。这种需求也受到熟练劳动力短缺和相关成本的制约。考虑到这些,人们正在努力利用人工智能(AI)和深度学习(DL)算法的进步,自动化和改进当前的作物收获过程。本文探讨了各种机器人收获系统,这些系统已经实施或计划利用这些技术来检测作物,导航到作物并以可靠的方式有效地收获作物。本文阐述了收获的作物,研究了人工智能/深度学习方法的选择标准,以及在实地实施中各自的好处和面临的挑战。最后,本文阐述了选择这种方法的可能指标,并发现卷积神经网络(CNN)基于其鲁棒性和性能是这种应用中深度学习方法的热门选择。
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Artificial Intelligence and Deep Learning Applications in Crop Harvesting Robots -A Survey
With the ever-growing population, demand of good quality food has also increased. This demand is also constrained by shortage of skillful labor & involved costs. Considering these, efforts have been made to automate and improve current crop harvesting processes, using advancements in artificial intelligence (AI) and deep learning (DL) algorithms. This paper explores various robotic harvesting systems, which have already implemented or plan to utilize such techniques to detect a crop, navigate to it and efficiently harvest it in a reliable way. The paper states the harvested crop, investigates the selection criteria of an AI/ DL method, the respective benefits & challenges faced in its field implementation. Lastly, the paper states the possible metrics for selection of such a method and finds that Convoluted Neural Networks (CNN) are a popular choice of DL method for such applications based on their robustness and performance.
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