Smart Agriculture ›› 2022, Vol. 4 ›› Issue (3): 24-41.doi: 10.12133/j.smartag.SA202206010
段罗佳1,2,3,4(), 杨福增1,2,3(
), 闫彬1,2,3, 史帅旗1,2,3, 秦纪凤1,2,3
收稿日期:
2022-06-21
出版日期:
2022-09-30
基金项目:
作者简介:
段罗佳(1990-),男,博士研究生,工程师,研究方向为智能化农业机械装备技术。E-mail:duanluojia@126.com
通信作者:
杨福增(1966-),男,博士,教授,研究方向为智能化农业机械装备技术。E-mail:yangfzkm@nwafu.edu.cn
DUAN Luojia1,2,3,4(), YANG Fuzeng1,2,3(
), YAN Bin1,2,3, SHI shuaiqi1,2,3, QIN jifeng1,2,3
Received:
2022-06-21
Online:
2022-09-30
Foundation items:
Shaanxi Province Major Science and Technology Breakthrough Program (202d0zdzx03-04-01)
About author:
DUAN Luojia, E-mail:duanluojia@126.com
Corresponding author:
YANG Fuzeng, E-mail:yangfzkm@nwafu.edu.cn
摘要:
苹果产业作为苹果主产区经济发展的支柱产业,为当地果农增收、农业增效做出了重要贡献。随着产业的转型升级,苹果生产机械化和智能化的发展程度将影响其经济效益。为推进苹果生产智能化技术研究与智能装备研发,本文概述了苹果生产各个环节机械化水平,阐述了动力底盘、除草装备、收获装备等苹果生产装备主要技术特点,归纳了自动调平与控制、自主导航、自动避障、杂草识别、杂草去除、苹果识别、苹果定位、苹果分离等技术分别在智能化动力底盘、智能除草装备、苹果采收机器人上的研究与应用进展,并阐明了上述3种智能装备关键技术的基本原理和特点。在此基础上,指出了目前苹果生产智能装备技术面临的挑战,并提出了发展建议。
中图分类号:
段罗佳, 杨福增, 闫彬, 史帅旗, 秦纪凤. 苹果生产智能底盘与除草及收获装备技术研究进展[J]. 智慧农业(中英文), 2022, 4(3): 24-41.
DUAN Luojia, YANG Fuzeng, YAN Bin, SHI shuaiqi, QIN jifeng. Research Progress of Apple Production Intelligent Chassis and Weeding and Harvesting Equipment Technology[J]. Smart Agriculture, 2022, 4(3): 24-41.
表2
四种类型的苹果识别机器学习算法比较
机器学习算法 | 特点 | 不足 |
---|---|---|
支持向量机 | 1.是一种非参数方法,具有一定的灵活性 2.可实现复杂功能,同时又能适应过度拟合 | 1.针对大批量果树图像的学习策略难以实施 2.解决青苹果、红苹果、树枝与树叶等多目标分类问题尚存在一定困难 |
神经网络 | 1.可以维持非线性算法的高精度 2.确保结果最佳逼近、全局最优、收敛速度快 | 1.可能会出现过拟合、中心难定、学习率偏低 2.网络运行效率和识别精度难以满足要求 |
聚类算法 | 1.技术简单、聚类相似输出 2.可被多层堆栈、效果直观 | 1.没有进行全局优化 2.某些情况下,随层数增加会失效,收益递减 |
深度学习 | 1.可以自动提取参数。 2.封闭静态环境,训练效果好,精度高。 | 1.对于动态环境效果较差,训练效果不好 2.需要大量数据进行训练,训练结果难以迁移 |
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