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

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 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.

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

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Growth,nutritional quality,and energy use efficiency in two lettuce cultivars as influenced by white plus red versus red plus blue LEDs

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International Journal of Agricultural and Biological Engineering 2020年第2期13卷 33-40页

作者： Zhengnan Yan Dongxian He Genhua Niu Qing Zhou Yinghua Qu Key Laboratory of Agricultural Engineering in Structure and Environment of Ministry of Agriculture and Rural Affairs China Agricultural UniversityBeijing 100083China Texas A&M AgriLife Research Center at Dallas Texas A&M University SystemDallasTX 75252USA

Red plus blue light-emitting diodes(LEDs)are commonly applied in plant factories with artificial lighting due to photosynthetic pigments,which absorb strongly in red and blue light regions of the spectrum.However,plants grown under natural environment are used to utilizing broad-wide spectrum by long-term evolution.In order to examine the effects of addition light added in red plus blue LEDs or white LEDs,green and purple leaf lettuces(Lactuca sativa L.cv.Lvdie and Ziya)were hydroponically cultivated for 20 days under white LEDs,white plus red LEDs,red plus blue LEDs,and red plus blue LEDs supplemented with ultraviolet,green or far-red light,respectively.The results indicated that the addition of far-red light in red plus blue LEDs increased leaf fresh and dry weights of green leaf lettuce by 28%and 34%,respectively.Addition of ultraviolet light did not induce any differences in growth and energy use efficiency in both lettuce cultivars,while supplementing green light with red plus blue LEDs reduced the vitamin C content of green leaf lettuce by 44%and anthocyanin content of purple leaf lettuce by 30%compared with red plus blue LEDs,respectively.Spectral absorbencies of purple leaf lettuce grown under red plus blue LEDs supplemented with green light were lower in green light region compared with those grown under red plus blue LEDs,which was associated with anthocyanin contents.White plus red LEDs significantly increased leaf fresh and dry weights of purple leaf lettuce by 25%,and no significant differences were observed in vitamin C and nitrate contents compared with white LEDs.Fresh weight,light and electrical energy use efficiencies of hydroponic green and purple leaf lettuces grown under white plus red LEDs were higher or no significant differences compared with those grown under red plus blue LEDs.In conclusion,white plus red LEDs were suggested to substitute for red plus blue LEDs in hydroponic lettuce(cv.Lvdie and Ziya)production in plant factories with artificial lighting.

关键词： plant factory hydroponic lettuce light-emitting diodes(LED) light energy use efficiency artificial lighting absorption spectrum anthocyanin content photosynthetic pigments

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Effects of cutter parameters on shearing stress for lettuce harvesting using a specially developed fixture

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International Journal of Agricultural and Biological Engineering 2021年第4期14卷 152-158页

作者： Yongjie Cui Wenqi Wang Minghui Wang Yidong Ma Longsheng Fu College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling 712100ShaanxiChina College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471003HenanChina

To investigate the optimal parameters combination of reciprocating cutter for harvesting hydroponic lettuce automatically,a shear fixture was designed for cutting lettuce stems on a universal materials tester.Effects of blade distance,sliding cutting angle,skew cutting angle,and shearing angle on shearing stress were investigated in this study.The orders of the significance of a single factor and double factors were analyzed using the response surface methodology(RSM).A scanning electron microscope was used to observe the microstructure of the lettuce stem to analyze the shearing characteristics at the microscopic level.The RSM results showed that the order of significance for single factors was(i)sliding cutting angle,(ii)shearing angle,(iii)skew cutting angle,and(iv)blade distance.The sliding cutting angle had a highly significant influence on the shearing stress.The order of significance for double factors was(i)blade distance and shearing angle,(ii)sliding cutting angle and skew cutting angle,and(iii)the sliding cutting angle and shearing angle.A quadratic model of the factors and shearing stress was built according to the response-surface results.The optimized combination of factors that gives the minimum shearing stress was observed that it reduced 69.9%of the maximum shearing stress value.This research can provide a reference for designing lettuce-cutting devices.

关键词： Lactuca sativa hydroponic lettuce cutting optimization response surface method micro-structure

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