Background:Biochanin A is an excellent dietary isoflavone that has the concomitant function of both medicine and foodstuff.The attenuation function of biochanin A on blood-brain barrier(BBB)damage induced by cerebral ...Background:Biochanin A is an excellent dietary isoflavone that has the concomitant function of both medicine and foodstuff.The attenuation function of biochanin A on blood-brain barrier(BBB)damage induced by cerebral ischemia-reperfusion remains unclear.Methods:C57BL/6 mice were subjected to 1 h middle cerebral artery occlusion(MCAO)followed by 24 h reperfusion.The infarct volume of the brain was stained by TTC,while leakage of the brain was quantitatively stained by Evans blue,and the neurologic deficit score was measured.Microglial-induced morphologic changes were observed via immunofluorescence staining,and rolling and adhering leukocytes in venules were observed via two-photon imaging,while the inner fluorescein isothiocyanate-albumin of venules were compared with those of surrounding interstitial area through venular albumin leakage.Results:The attenuation effect of biochanin A on tight junction injury was compared in ischemia-reperfusion mice or conventional knockdown of leucine-richα2-glycoprotein 1(Lrg1)mice.Biochanin A could ameliorate BBB injury in mice with cerebral ischemiareperfusion in a dose-dependent manner by strengthening the immunostaining volume of occludin,claudin-5,and zonula occludens-1.The amoeba morphologic changes of microglial combined with the elevated expression of Lrg1 could be relieved under the treatment of biochanin A.Biochanin A played a countervailing role on the rolling leukocytes in the vessel,while the leakage of blood vessels was reduced.Biochanin A diminished its functions to further improved attenuation for tight junction injury on conventional Lrg1-knockout mice,as well as the inhibition effects on TGF-β1,and the phosphorylation of suppressor of mothers against decapentaplegic 2(Smad2)/Smad2 via western blot assay.Conclusion:Biochanin A could alleviate tight junction injury induced by cerebral ischemiareperfusion and blocked the Lrg1/TGF-β/Smad2 pathway to modulate leukocyte migration patterns.展开更多
The attenuation function of Dalbergia odorifera leaves on cerebral ischemia-reperfusion(I/R)is little known.The candidate targets for the Chinese herb were extracted from brain tissues through the high-affinity chroma...The attenuation function of Dalbergia odorifera leaves on cerebral ischemia-reperfusion(I/R)is little known.The candidate targets for the Chinese herb were extracted from brain tissues through the high-affinity chromatography.The molecular mechanism of D.odorifera leaves on cerebral I/R was investigated.Methods:Serial affinity chromatography based on D.odorifera leaves extract(DLE)affinity matrices were applied to find specific binding proteins in the brain tissues implemented on C57BL/6 mice by intraluminal middle cerebral artery occlusion for 1 h and reperfusion for 24 h.Specific binding proteins were subjected to mass-spectrometry to search for the differentially expressed proteins between control and DLE-affinity matrices.The hub genes were screened based on weighted gene co-expression network analysis(WGCNA).Then,predictive biology and potential experimental verification were performed for the candidate genes.The protective role of DLE in blood-brain barrier damage in cerebral I/R mice was evaluated by the leakage of Evans blue,western blotting,immunohistochemistry,and immunofluorescent staining.Results:952 differentially expressed proteins were classified into seven modules based on WGCNA under soft threshold 6.Based on WGCNA,AKT1,PIK3CA,NOS3,SMAD3,SMAD1,IL6,MAPK1,TGFBR2,TGFBR1,MAPK3,IGF1R,LRG1,mTOR,ROCK1,TGFB1,IL1B,SMAD2,and SMAD518 candidate hub proteins were involved in turquoise module.TGF-β,MAPK,focal adhesion,and adherens junction signaling pathway were associated with candidate hub proteins.Gene ontology analysis demonstrated that candidate hub proteins were related to the TGF-βreceptor signaling pathway,common-partner SMAD protein phosphorylation,etc.DLE could significantly reduce the leakage of Evans blue in mice with cerebral I/R,while attenuating the expression of occludin,claudin-5,and zonula occludens-1.Western blotting demonstrated that regulation of TGF-β/SMAD signaling pathway played an essential role in the protective effect of DLE.Conclusion:Thus,a number of candidate hub proteins were identified based on DLE affinity chromatography through WGCNA.DLE could attenuate the dysfunction of bloodbrain barrier in the TGF-β/SMAD signaling pathway induced by cerebral I/R.展开更多
Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic app...Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic apple picking.Detection accuracy has a great influence on localization results.Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex environments.With the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly improved.Therefore,a deep learningbased method was developed to accurately detect and locate the position of fruit.For different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment cost.Hence,this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning.First,apples of binocular images were detected by Faster R-CNN.After that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit.Furthermore,template matching with parallel polar line constraint was used to match apples in left and right images.Finally,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle.In this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an image.Meanwhile,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization.Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,respectively.Results indicated that the proposed improved binocular localization method is promising for fruit localization。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
The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for h...The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for harvesting kiwifruit.To improve the efficiency of a harvesting robot for picking kiwifruit,we designed an end-effector,which we report herein along with the results of tests to verify its operation.By using the established method of automated picking discussed in the literature and which is based on the characteristics of kiwifruit,we propose an automated method to pick kiwifruit that consists of separating the fruit from its stem on the tree.This method is experimentally verified by using it to pick clustered kiwifruit in a scaffolding canopy cultivation.In the experiment,the end-effector approaches a fruit from below and then envelops and grabs it with two bionic fingers.The fingers are then bent to separate the fruit from its stem.The grabbing,picking,and unloading processes are integrated,with automated picking and unloading performed using a connecting rod linkage following a trajectory model.The trajectory was analyzed and validated by using a simulation implemented in the software Automatic Dynamic Analysis of Mechanical Systems(ADAMS).In addition,a prototype of an end-effector was constructed,and its bionic fingers were equipped with fiber sensors to detect the best position for grabbing the kiwifruit and pressure sensors to ensure that the damage threshold was respected while picking.Tolerances for size and shape were incorporated by following a trajectory groove from grabbing and picking to unloading.The end-effector separates clustered kiwifruit and automatically grabs individual fruits.It takes on average 4–5 s to pick a single fruit,with a successful picking rate of 94.2%in an orchard test featuring 240 samples.This study shows the grabbing–picking–unloading robotic end-effector has significant potential to facilitate the harvesting of kiwifruit.展开更多
The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,whic...The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,which can promote industrialization and improve industrial value and market prospects.Currently,New Zealand,Italy,Chile,and China carry out research into the mechanism of kiwifruit production.This review describes in detail the current state of the art of pollination,harvesting and grading equipment,including detection and identification,non-destructive end effector,harvesting robots and grading devices.Process technologies that include artificial pollination,harvest mechanization,grading and standardization of production problems are analysed and compared.These problems directly affect the quality of kiwifruit products.Finally,to solve the various problems that the kiwifruit industry experiences,it is necessary to accelerate the development of mechanized kiwifruit production,realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future.Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops.展开更多
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 ...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.展开更多
To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geomet...To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geometric characteristics,pulling force,and root cutting force were studied for harvesting hydroponic lettuce.The pulling force was examined by a tensile experiment,while the root cutting force was investigated by a shear experiment on the electronic universal testing machine.The moisture content of hydroponic lettuce was obtained by direct drying.Experiment data were processed using regression analysis and mathematical statistics method.A regression equation and the law of numerical distribution were obtained.The results showed that the geometric size of different hydroponic lettuce had little difference,and the distribution of physical parameters was concentrated.Moisture 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 downward.The minimum average root cutting force in the experiment was 1.41 N.The average pulling force was 13 N.This study provides adequate theoretical support for the design of the automatic harvesting machine of hydroponic lettuce.展开更多
基金supported by a Foundation Project:National Natural Science Foundation of China(Nos.82100417,81760094),ChinaThe Foundation of Jiangxi Provincial Department of Science and Technology Project(Nos.20202ACBL206001,20212BAB206022,20181BAB205026).
文摘Background:Biochanin A is an excellent dietary isoflavone that has the concomitant function of both medicine and foodstuff.The attenuation function of biochanin A on blood-brain barrier(BBB)damage induced by cerebral ischemia-reperfusion remains unclear.Methods:C57BL/6 mice were subjected to 1 h middle cerebral artery occlusion(MCAO)followed by 24 h reperfusion.The infarct volume of the brain was stained by TTC,while leakage of the brain was quantitatively stained by Evans blue,and the neurologic deficit score was measured.Microglial-induced morphologic changes were observed via immunofluorescence staining,and rolling and adhering leukocytes in venules were observed via two-photon imaging,while the inner fluorescein isothiocyanate-albumin of venules were compared with those of surrounding interstitial area through venular albumin leakage.Results:The attenuation effect of biochanin A on tight junction injury was compared in ischemia-reperfusion mice or conventional knockdown of leucine-richα2-glycoprotein 1(Lrg1)mice.Biochanin A could ameliorate BBB injury in mice with cerebral ischemiareperfusion in a dose-dependent manner by strengthening the immunostaining volume of occludin,claudin-5,and zonula occludens-1.The amoeba morphologic changes of microglial combined with the elevated expression of Lrg1 could be relieved under the treatment of biochanin A.Biochanin A played a countervailing role on the rolling leukocytes in the vessel,while the leakage of blood vessels was reduced.Biochanin A diminished its functions to further improved attenuation for tight junction injury on conventional Lrg1-knockout mice,as well as the inhibition effects on TGF-β1,and the phosphorylation of suppressor of mothers against decapentaplegic 2(Smad2)/Smad2 via western blot assay.Conclusion:Biochanin A could alleviate tight junction injury induced by cerebral ischemiareperfusion and blocked the Lrg1/TGF-β/Smad2 pathway to modulate leukocyte migration patterns.
基金supported by National Natural Science Foundation of China(Nos.82100417,81760094,81760724)The Foundation of Jiangxi Provincial Department of Science and Technology Project(Nos.20202ACBL206001,20212BAB206022,20181BAB205026)+1 种基金Youth Project of Jiangxi Education Department(No.GJJ200217)Open Project of Key Laboratory of Modern of TCM,Ministry of Education Jiangxi University of Traditional Chinese Medicine(TCM-2019010).
文摘The attenuation function of Dalbergia odorifera leaves on cerebral ischemia-reperfusion(I/R)is little known.The candidate targets for the Chinese herb were extracted from brain tissues through the high-affinity chromatography.The molecular mechanism of D.odorifera leaves on cerebral I/R was investigated.Methods:Serial affinity chromatography based on D.odorifera leaves extract(DLE)affinity matrices were applied to find specific binding proteins in the brain tissues implemented on C57BL/6 mice by intraluminal middle cerebral artery occlusion for 1 h and reperfusion for 24 h.Specific binding proteins were subjected to mass-spectrometry to search for the differentially expressed proteins between control and DLE-affinity matrices.The hub genes were screened based on weighted gene co-expression network analysis(WGCNA).Then,predictive biology and potential experimental verification were performed for the candidate genes.The protective role of DLE in blood-brain barrier damage in cerebral I/R mice was evaluated by the leakage of Evans blue,western blotting,immunohistochemistry,and immunofluorescent staining.Results:952 differentially expressed proteins were classified into seven modules based on WGCNA under soft threshold 6.Based on WGCNA,AKT1,PIK3CA,NOS3,SMAD3,SMAD1,IL6,MAPK1,TGFBR2,TGFBR1,MAPK3,IGF1R,LRG1,mTOR,ROCK1,TGFB1,IL1B,SMAD2,and SMAD518 candidate hub proteins were involved in turquoise module.TGF-β,MAPK,focal adhesion,and adherens junction signaling pathway were associated with candidate hub proteins.Gene ontology analysis demonstrated that candidate hub proteins were related to the TGF-βreceptor signaling pathway,common-partner SMAD protein phosphorylation,etc.DLE could significantly reduce the leakage of Evans blue in mice with cerebral I/R,while attenuating the expression of occludin,claudin-5,and zonula occludens-1.Western blotting demonstrated that regulation of TGF-β/SMAD signaling pathway played an essential role in the protective effect of DLE.Conclusion:Thus,a number of candidate hub proteins were identified based on DLE affinity chromatography through WGCNA.DLE could attenuate the dysfunction of bloodbrain barrier in the TGF-β/SMAD signaling pathway induced by cerebral I/R.
基金the National Natural Science of China(32171897)Youth Science and Technology Nova Program in Shaanxi Province of China(2021KJXX-94)+1 种基金Science and Technology Promotion Program of Northwest A&F University(TGZX2021-29)Recruitment Program of High-End Foreign Experts of the State Administration of Foreign Experts Affairs,Ministry of Science and Technology,China(G20200027075).
文摘Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic apple picking.Detection accuracy has a great influence on localization results.Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex environments.With the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly improved.Therefore,a deep learningbased method was developed to accurately detect and locate the position of fruit.For different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment cost.Hence,this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning.First,apples of binocular images were detected by Faster R-CNN.After that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit.Furthermore,template matching with parallel polar line constraint was used to match apples in left and right images.Finally,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle.In this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an image.Meanwhile,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization.Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,respectively.Results indicated that the proposed improved binocular localization method is promising for fruit localization。
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.
基金This research was conducted in the College of Mechanical and Electronic Engineering,Northwest A&F University,and was supported by research grants from the General Program of the National Natural Science Foundation of China(61175099).
文摘The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for harvesting kiwifruit.To improve the efficiency of a harvesting robot for picking kiwifruit,we designed an end-effector,which we report herein along with the results of tests to verify its operation.By using the established method of automated picking discussed in the literature and which is based on the characteristics of kiwifruit,we propose an automated method to pick kiwifruit that consists of separating the fruit from its stem on the tree.This method is experimentally verified by using it to pick clustered kiwifruit in a scaffolding canopy cultivation.In the experiment,the end-effector approaches a fruit from below and then envelops and grabs it with two bionic fingers.The fingers are then bent to separate the fruit from its stem.The grabbing,picking,and unloading processes are integrated,with automated picking and unloading performed using a connecting rod linkage following a trajectory model.The trajectory was analyzed and validated by using a simulation implemented in the software Automatic Dynamic Analysis of Mechanical Systems(ADAMS).In addition,a prototype of an end-effector was constructed,and its bionic fingers were equipped with fiber sensors to detect the best position for grabbing the kiwifruit and pressure sensors to ensure that the damage threshold was respected while picking.Tolerances for size and shape were incorporated by following a trajectory groove from grabbing and picking to unloading.The end-effector separates clustered kiwifruit and automatically grabs individual fruits.It takes on average 4–5 s to pick a single fruit,with a successful picking rate of 94.2%in an orchard test featuring 240 samples.This study shows the grabbing–picking–unloading robotic end-effector has significant potential to facilitate the harvesting of kiwifruit.
基金supported by research grants from the General Program of the National Natural Science Foundation of China(61175099).
文摘The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,which can promote industrialization and improve industrial value and market prospects.Currently,New Zealand,Italy,Chile,and China carry out research into the mechanism of kiwifruit production.This review describes in detail the current state of the art of pollination,harvesting and grading equipment,including detection and identification,non-destructive end effector,harvesting robots and grading devices.Process technologies that include artificial pollination,harvest mechanization,grading and standardization of production problems are analysed and compared.These problems directly affect the quality of kiwifruit products.Finally,to solve the various problems that the kiwifruit industry experiences,it is necessary to accelerate the development of mechanized kiwifruit production,realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future.Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops.
基金This research was supported by the Key Research and Development Program in Shaanxi Province of China(Grant No.2018TSCXL-NY-05-04,2019ZDLNY02-04)Science and Technology Program in Yulin City of China(Grant No.CXY-2020-076).
文摘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.
基金supported by the Key Research and Development Program in Shaanxi Province of China[grant number 2018TSCXL-NY-05-04,2019ZDLNY02-04].
文摘To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geometric characteristics,pulling force,and root cutting force were studied for harvesting hydroponic lettuce.The pulling force was examined by a tensile experiment,while the root cutting force was investigated by a shear experiment on the electronic universal testing machine.The moisture content of hydroponic lettuce was obtained by direct drying.Experiment data were processed using regression analysis and mathematical statistics method.A regression equation and the law of numerical distribution were obtained.The results showed that the geometric size of different hydroponic lettuce had little difference,and the distribution of physical parameters was concentrated.Moisture 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 downward.The minimum average root cutting force in the experiment was 1.41 N.The average pulling force was 13 N.This study provides adequate theoretical support for the design of the automatic harvesting machine of hydroponic lettuce.
