The sorting system applies multi-sensor technology,PLC technology,pneumatic technology and frequency converter technology to realize the efficient automatic sorting of workpieces and solve the problem of automatic sor...The sorting system applies multi-sensor technology,PLC technology,pneumatic technology and frequency converter technology to realize the efficient automatic sorting of workpieces and solve the problem of automatic sorting of more complex shaped products.Through running test,the system has high efficiency,reliable operation,strong practicability,and great application value in automatic production lines such as mechanical processing,electronic assembly and article circulation.展开更多
Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstra...Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple 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 images.Image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It 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%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.展开更多
基金City College of Dongguan University of Technology Youth Teacher Development Fund(2019QJY003Z)Key Cultivating Disciplines of Guangdong Province(Document No.45,2017)City College of Dongguan University of Technology Youth Teacher Development Fund(2020QJY001Z).
文摘The sorting system applies multi-sensor technology,PLC technology,pneumatic technology and frequency converter technology to realize the efficient automatic sorting of workpieces and solve the problem of automatic sorting of more complex shaped products.Through running test,the system has high efficiency,reliable operation,strong practicability,and great application value in automatic production lines such as mechanical processing,electronic assembly and article circulation.
基金the Science and Technology Program in Yulin City of China(CXY-2020-076,CXY-2019-129)Youth Science and Technology Nova Program in Shaanxi Province of China(2021KJXX-94)+1 种基金Key Research and Development Program of Shaanxi(2021NY-135)Recruitment Program of High-End Foreign Experts of the State Administration of Foreign Experts Affairs,Ministry of Science and Technology,China(G20200027075)。
文摘Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple 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 images.Image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It 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%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.
