检索结果-南通市图书馆

Physical and mechanical properties of hydroponic lettuce for automatic harvesting

Information Processing in Agriculture 2021年第4期8卷 550-559页

作者： Wenqi wang Yidong Ma Longsheng Fu Yongjie Cui Yaqoob majeed College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling 712100China College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471003China Centre for Precision and Automated Agricultural Systems Washington State UniversityProsserWA 99350USA

To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and *** content of stem,root and leaf,geometric characteristics,pulling force,and root cutting force were studied for harvesting hydroponic *** pulling force was examined by a tensile experiment,while the root cutting force was investigated by a shear experiment on the electronic universal testing *** moisture content of hydroponic lettuce was obtained by direct *** data were processed using regression analysis and mathematical statistics method.A regression equation and the law of numerical distribution were *** results showed that the geometric size of different hydroponic lettuce had little difference,and the distribution of physical parameters was *** content was found statistically similar in stem and root(around 91%),while the highest moisture content was found in the leaf of 95.73%.The root cutting force decrease with the increase of cutting speed and decrease with the cutting position move *** minimum average root cutting force in the experiment was 1.41 *** average pulling force was 13 *** study provides adequate theoretical support for the design of the automatic harvesting machine of hydroponic lettuce.

关键词： Greenhouse vegetable Harvester Variance analysis Response surface

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Detection of abnormal hydroponic lettuce leaves based on image processing and machine learning

引用

Information Processing in Agriculture 2023年第1期10卷 1-10页

作者： Ruizhe Yang Zhenchao Wu Wentai Fang Hongliang Zhang Wenqi wang Longsheng Fu Yaqoob majeed Rui Li Yongjie Cui College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Xi’an Agriculture Machinery Management and Extension Station Xi’anShaanxi 710065China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China Faculty of Agricultural Engineering and Technology University of AgricultureFaisalabad 38000Pakistan Center for Precision and Automated Agricultural Systems Washington State UniversityProsserWA 99350USA Suide County Lanhuahua Ecological Food Co. Ltd.SuideShaanxi 718000China

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic *** and rotten leaves are main types of abnormal leaves in hydroponic *** study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,*** Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce *** binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models *** showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training *** took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,*** was more robust than MLR in detecting yellow and rotten leaves of ***,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

关键词： Hydroponic lettuce Machine learning Image processing Abnormal leaves Automatic sorting

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：