机器学习在无人农场中的应用现状与展望

doi:10.13733/j.jcam.issn.2095-5553.2021.10.26

摘要/Abstract

摘要： 随着机器学习在生物信息、人脸识别等领域的成功应用,其也为无人农场的发展提供动力。首先阐述无人农场和机器学习的基础概念,同时分别在种植业和畜牧业两个方面对机器学习的应用进行分析,在种植业方面阐述其在田间杂草识别、作物病虫害检测、作物产量预测的应用,在畜牧业方面分析机器学习在鱼类、猪等牲畜的精准识别分类、鱼类的喂食决策系统以及鸡、牛的生产线预测方面的应用现状;提出机器学习存在训练样本难获取、难标记的问题,嵌入式芯片的性能缺陷,以及专业人才缺乏等劣势;应建立通用的无人农场数据库,研究可以预测动物的健康状况以及对动物的生长环境状况进行实时监测的专家系统,还应加强机器学习的嵌入式研究,以及结合5G、大数据、传感器等技术的机器学习将成为无人农场未来的研究方向。本文对机器学习在无人农场的应用现状、问题及展望进行总结叙述,期望为以后的进一步研究提供参考。

关键词: 机器学习, 无人农场, 作物管理, 畜牧养殖, 精准识别, 生产预测

中图分类号:

TP181
S24

王宝聚, 兰玉彬, 陈蒙蒙, 柳宝虎, 王国宾, 刘海涛, . 机器学习在无人农场中的应用现状与展望[J]. 中国农机化学报, 2021, 42(10): 186-192+217.

Wang Baoju, Lan Yubin, Chen Mengmeng, Liu Baohu, Wang Guobin, Liu Haitao.. Application status and prospect of machine learning in unmanned farm[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(10): 186-192+217.

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