The stable production of seedlings is very important for seedling growers.Predicting the growth of seedlings helps growers promptly adjust management strategies and production expectations.Traditional methods rely on ...The stable production of seedlings is very important for seedling growers.Predicting the growth of seedlings helps growers promptly adjust management strategies and production expectations.Traditional methods rely on historical growth data or assess current plant physiological parameters to estimate future growth.This study aims to predict future images directly from historical growth images of tomato seedlings.Specifically,a dataset of 10-d image sequences of tomato seedlings was collected.Then,an algorithm based on several neural networks was applied to predict the images of the next 5 d based on the images of the first 5 d.The algorithm was composed of a causal long short-term memory(LSTM)unit,a gradient highway unit(GHU),and a pix2pix unit.The experimental results showed that the introduction of a Generative Adversarial Network(GAN)further enhanced the clarity and realism of the predicted images,ensuring higher quality and more accurate visual results.From the perspective of image similarity,the average mean squared error(MSE)reached 394.97 and the average structural similarity(SSIM)reached 0.90 over 5 d.From the perspective of biological information,the average prediction errors of the plant area were 1.7,1.4,1.5,0.9,and 3.2 cm^(2)over the 5 d,and the average prediction errors of plant height were 1.7,1.9,4.6,6.9,and 4.5 mm,respectively.The extracted biological information such as plant area and height showed good following performance compared with the real growth information.The research results show that predicting future plant images from historical images has the potential to become a useful tool for nursery growers to adjust management strategies and production expectations.展开更多
The silicon-based arrayed waveguide grating(AWG)is widely used due to its compact footprint and its compatibility with the mature CMOS process.However,except for AWGs with ridged waveguides of a few micrometers of cro...The silicon-based arrayed waveguide grating(AWG)is widely used due to its compact footprint and its compatibility with the mature CMOS process.However,except for AWGs with ridged waveguides of a few micrometers of cross section,any small process error will cause a large phase deviation in other AWGs,resulting in an increasing cross talk.In this paper,an ultralow cross talk AWG via a tunable microring resonator(MRR)filter is demonstrated on the SOI platform.The measured insertion loss and minimum adjacent cross talk of the designed AWG are approximately 3.2 and-45.1 d B,respectively.Compared with conventional AWG,its cross talk is greatly reduced.展开更多
Light detection and ranging(LiDAR)serves as one of the key components in the fields of autonomous driving,surveying mapping,and environment detection.Conventionally,dense points clouds are pursued by LiDAR systems to ...Light detection and ranging(LiDAR)serves as one of the key components in the fields of autonomous driving,surveying mapping,and environment detection.Conventionally,dense points clouds are pursued by LiDAR systems to provide high-definition 3D images.However,the LiDAR is typically used to produce abundant yet redundant data for scanning the homogeneous background of scenes,resulting in power waste and excessive processing time.Hence,it is highly desirable for a LiDAR system to“gaze”at the target of interest by dense scanning and rough sparse scans on the uninteresting areas.Here,we propose a LiDAR structure based on an optical phased array(OPA)with reconfigurable apertures to achieve such a gaze scanning function.By virtue of the cascaded optical switch integrated on the OPA chip,a 64-,128-,192-,or 256-channel antenna can be selected discretionarily to construct an aperture with variable size.The corresponding divergence angles for the far-field beam are 0.32°,0.15°,0.10°,and 0.08°,respectively.The reconfigurable-aperture OPA enables the LiDAR system to perform rough scans via the large beam spots prior to fine scans of the target by using the tiny beam spots.In this way,the OPA-based LiDAR can perform the“gaze”function and achieve full-range scanning efficiently.The scanning time and power consumption can be reduced by 1/4 while precise details of the target are maintained.Finally,we embed the OPA into a frequency-modulated continuous-wave(FMCW)system to demonstrate the“gaze”function in beam scanning.Experiment results show that the number of precise scanning points can be reduced by 2/3 yet can obtain the reasonable outline of the target.The reconfigurable-aperture OPA(RA-OPA)can be a promising candidate for the applications of rapid recognition,like car navigation and robot vision.展开更多
Germanium(Ge)-silicon(Si)-based avalanche photodetectors(APDs)featured by a high absorption coefficient in the near-infrared band have gained wide applications in laser ranging,free space communication,quantum communi...Germanium(Ge)-silicon(Si)-based avalanche photodetectors(APDs)featured by a high absorption coefficient in the near-infrared band have gained wide applications in laser ranging,free space communication,quantum communication,and so on.However,the Ge APDs fabricated by the complementary metal oxide semiconductor(CMOS)process suffer from a large dark current and limited responsivity,imposing a critical challenge on integrated silicon photonic links.In this work,we propose a p-i-n-i-n type Ge APD consisting of an intrinsic germanium layer functioning as both avalanche and absorption regions and an intrinsic silicon layer for dark current reduction.Consequently,a Ge APD with a low dark current,low bias voltage,and high responsivity can be obtained via a standard silicon photonics platform.In the experimental measurement,the Ge APD is characterized by a high primary responsivity of 1.1 A/W with a low dark current as low as 7.42 nA and a dark current density of 6.1×10^(−11) A∕μm^(2) at a bias voltage of−2 V.In addition,the avalanche voltage of the Ge APD is−8.4 V and the measured 3 dB bandwidth of the Ge APD can reach 25 GHz.We have also demonstrated the capability of data reception on 32 Gbps non-return-to-zero(NRZ)optical signal,which has potential application for silicon photonic data links.展开更多
To solve the problem of high labour costs in the strawberry picking process,the approach of a strawberry picking robot to identify and find strawberries is suggested in this study.First,1000 images including mature,im...To solve the problem of high labour costs in the strawberry picking process,the approach of a strawberry picking robot to identify and find strawberries is suggested in this study.First,1000 images including mature,immature,single,multiple,and occluded strawberries were collected,and a two-stage detection Mask R-CNN instance segmentation network and a one-stage detection YOLOv3 target detection network were used to train a strawberry identification model which classified strawberries into two categories:mature and immature.The accuracy ratings for YOLOv3 and Mask R-CNN were 93.4%and 94.5%,respectively.Second,the ZED stereo camera,triangulation,and a neural network were used to locate the strawberry in three dimensions.YOLOv3 identification accuracy was 3.1 mm,compared to Mask R-CNN of 3.9 mm.The strawberry detection and positioning method proposed in this study may effectively be used to supply the picking robot with a precise location of the ripe strawberry.展开更多
In order to improve the deposition and uniformity of the pesticide sprayed by the agricultural spraying drone,this study designed a novel spraying system,combining air-assisted spraying system with electrostatic techn...In order to improve the deposition and uniformity of the pesticide sprayed by the agricultural spraying drone,this study designed a novel spraying system,combining air-assisted spraying system with electrostatic technology.First,an air-assisted electrostatic centrifugal spray system was designed for agricultural spraying drones,including a shell,a diversion shell,and an electrostatic ring.Then,experiments were conducted to optimize the setting of the main parameters that affect the charge-to-mass ratio,and outdoor spraying experiments were carried out on the spraying effect of the air-assisted electrostatic centrifugal spray system.The results showed the optimum parameters were that the centrifugal rotation speed was 10000 r/min,the spray pressure was 0.3 MPa,the fan rotation speed was 14000 r/min,and the electrostatic generator voltage was 9 kV;The optimum charge-to-mass ratio of the spray system was 2.59 mC/kg.The average deposition density of droplets on the collecting platform was 366.1 particles/cm^(2) on the upper layer,345.1 particles/cm^(2) on the middle layer,and 322.5 particles/cm^(2) on the lower layer.Compared to the results of uncharged droplets on the upper,middle,and lower layers,the average deposition density was increased by 34.9%,30.4%,and 30.2%,respectively,and the uniformity of the distribution of the droplets at different collection points was better.展开更多
Automatically identifying the degradability of municipal solid waste(MSW)is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes.In this study,a cost-effective method ...Automatically identifying the degradability of municipal solid waste(MSW)is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes.In this study,a cost-effective method was proposed for the degradability identification of MSW.Firstly,the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site.Secondly,a lite convolutional neural network(CNN)model was built with only 3.37 million parameters,and then a total of eight models were trained on these datasets with and without the image augmentation operations,respectively.Finally,a degradability identification system was built for on-site composting,where the images were cut to different sizes of small squares for prediction,and the experiments were conducted to find the best combinations of the trained models and the cutting size.The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher,and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%,which indicated the capability of this cost-effective method to identify the degradability of MSW.展开更多
Fast assessment of the initial carbon to nitrogen ratio(C/N)of organic fraction of municipal solid waste(OFMSW)is an important prerequisite for automatic composting control to improve efficiency and stability of the b...Fast assessment of the initial carbon to nitrogen ratio(C/N)of organic fraction of municipal solid waste(OFMSW)is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process.In this study,a novel approach was proposed to estimate the C/N of OFMSW,where an instance segmentation model was applied to predict the masks for the waste images.Then,by combining the instance segmentation model with the depth-camera-based volume calculation algorithm,the volumes occupied by each type of waste were obtained,therefore the C/N could be estimated based on the properties of each type of waste.First,an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks(Mask R-CNN)model.Second,a volume measurement algorithm was proposed,where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property.Then the calculated volume was corrected with linear regression models.The results showed that the trained instance segmentation model performed well with average precision scores AP_(50)=82.9,AP_(75)=72.5,and mask intersection over unit(Mask IoU)=45.1.A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R2=0.97 and root mean square error RMSE=0.10.The relative average error was 0.42%and the maximum error was only 1.71%,which indicated this approach has potential for practical applications.展开更多
文摘The stable production of seedlings is very important for seedling growers.Predicting the growth of seedlings helps growers promptly adjust management strategies and production expectations.Traditional methods rely on historical growth data or assess current plant physiological parameters to estimate future growth.This study aims to predict future images directly from historical growth images of tomato seedlings.Specifically,a dataset of 10-d image sequences of tomato seedlings was collected.Then,an algorithm based on several neural networks was applied to predict the images of the next 5 d based on the images of the first 5 d.The algorithm was composed of a causal long short-term memory(LSTM)unit,a gradient highway unit(GHU),and a pix2pix unit.The experimental results showed that the introduction of a Generative Adversarial Network(GAN)further enhanced the clarity and realism of the predicted images,ensuring higher quality and more accurate visual results.From the perspective of image similarity,the average mean squared error(MSE)reached 394.97 and the average structural similarity(SSIM)reached 0.90 over 5 d.From the perspective of biological information,the average prediction errors of the plant area were 1.7,1.4,1.5,0.9,and 3.2 cm^(2)over the 5 d,and the average prediction errors of plant height were 1.7,1.9,4.6,6.9,and 4.5 mm,respectively.The extracted biological information such as plant area and height showed good following performance compared with the real growth information.The research results show that predicting future plant images from historical images has the potential to become a useful tool for nursery growers to adjust management strategies and production expectations.
基金supported by the National Key Research and Development Program of China(No.2018YFB2200500)the Yunnan Provincial Foundation Program(No.202201AT070202)the National Natural Science Foundation of China(No.62065010)。
文摘The silicon-based arrayed waveguide grating(AWG)is widely used due to its compact footprint and its compatibility with the mature CMOS process.However,except for AWGs with ridged waveguides of a few micrometers of cross section,any small process error will cause a large phase deviation in other AWGs,resulting in an increasing cross talk.In this paper,an ultralow cross talk AWG via a tunable microring resonator(MRR)filter is demonstrated on the SOI platform.The measured insertion loss and minimum adjacent cross talk of the designed AWG are approximately 3.2 and-45.1 d B,respectively.Compared with conventional AWG,its cross talk is greatly reduced.
基金Program for Jilin University Science and Technology Innovative Research Team(2021TD-39)Jilin Provincial Development and Reform Commission Project(2020C056)+2 种基金Major Scientific and Technological Program of Jilin Province(20210301014GX)National Natural Science Foundation of China(62105173,62105174,61934003,62090054)National Key Research and Development Program of China(2022YFB2804504)。
文摘Light detection and ranging(LiDAR)serves as one of the key components in the fields of autonomous driving,surveying mapping,and environment detection.Conventionally,dense points clouds are pursued by LiDAR systems to provide high-definition 3D images.However,the LiDAR is typically used to produce abundant yet redundant data for scanning the homogeneous background of scenes,resulting in power waste and excessive processing time.Hence,it is highly desirable for a LiDAR system to“gaze”at the target of interest by dense scanning and rough sparse scans on the uninteresting areas.Here,we propose a LiDAR structure based on an optical phased array(OPA)with reconfigurable apertures to achieve such a gaze scanning function.By virtue of the cascaded optical switch integrated on the OPA chip,a 64-,128-,192-,or 256-channel antenna can be selected discretionarily to construct an aperture with variable size.The corresponding divergence angles for the far-field beam are 0.32°,0.15°,0.10°,and 0.08°,respectively.The reconfigurable-aperture OPA enables the LiDAR system to perform rough scans via the large beam spots prior to fine scans of the target by using the tiny beam spots.In this way,the OPA-based LiDAR can perform the“gaze”function and achieve full-range scanning efficiently.The scanning time and power consumption can be reduced by 1/4 while precise details of the target are maintained.Finally,we embed the OPA into a frequency-modulated continuous-wave(FMCW)system to demonstrate the“gaze”function in beam scanning.Experiment results show that the number of precise scanning points can be reduced by 2/3 yet can obtain the reasonable outline of the target.The reconfigurable-aperture OPA(RA-OPA)can be a promising candidate for the applications of rapid recognition,like car navigation and robot vision.
基金National Key Research and Development Program of China(2022YFB2804504)National Natural Science Foundation of China(62090054,61934003,62105173,62105174)+2 种基金Major Scientific and Technological Program of Jilin Province(20210301014GX)Jilin Province Development and Reform Commission(2020C056)Program for Jilin University Science and Technology Innovative Research Team(JLUSTIRT,2021TD-39).
文摘Germanium(Ge)-silicon(Si)-based avalanche photodetectors(APDs)featured by a high absorption coefficient in the near-infrared band have gained wide applications in laser ranging,free space communication,quantum communication,and so on.However,the Ge APDs fabricated by the complementary metal oxide semiconductor(CMOS)process suffer from a large dark current and limited responsivity,imposing a critical challenge on integrated silicon photonic links.In this work,we propose a p-i-n-i-n type Ge APD consisting of an intrinsic germanium layer functioning as both avalanche and absorption regions and an intrinsic silicon layer for dark current reduction.Consequently,a Ge APD with a low dark current,low bias voltage,and high responsivity can be obtained via a standard silicon photonics platform.In the experimental measurement,the Ge APD is characterized by a high primary responsivity of 1.1 A/W with a low dark current as low as 7.42 nA and a dark current density of 6.1×10^(−11) A∕μm^(2) at a bias voltage of−2 V.In addition,the avalanche voltage of the Ge APD is−8.4 V and the measured 3 dB bandwidth of the Ge APD can reach 25 GHz.We have also demonstrated the capability of data reception on 32 Gbps non-return-to-zero(NRZ)optical signal,which has potential application for silicon photonic data links.
文摘To solve the problem of high labour costs in the strawberry picking process,the approach of a strawberry picking robot to identify and find strawberries is suggested in this study.First,1000 images including mature,immature,single,multiple,and occluded strawberries were collected,and a two-stage detection Mask R-CNN instance segmentation network and a one-stage detection YOLOv3 target detection network were used to train a strawberry identification model which classified strawberries into two categories:mature and immature.The accuracy ratings for YOLOv3 and Mask R-CNN were 93.4%and 94.5%,respectively.Second,the ZED stereo camera,triangulation,and a neural network were used to locate the strawberry in three dimensions.YOLOv3 identification accuracy was 3.1 mm,compared to Mask R-CNN of 3.9 mm.The strawberry detection and positioning method proposed in this study may effectively be used to supply the picking robot with a precise location of the ripe strawberry.
基金financially supported by the National Key Research and Development Program of China(Grant No.2018YFD0200800)the Key Research and Development Program of Hunan Province(Grant No.2018GK2013)+1 种基金Hunan Modern Agricultural Industry Technology Program(Grant No.201926)Innovation and Entrepreneurship Training Program of Hunan Agricultural University(Grant No.2019062x).
文摘In order to improve the deposition and uniformity of the pesticide sprayed by the agricultural spraying drone,this study designed a novel spraying system,combining air-assisted spraying system with electrostatic technology.First,an air-assisted electrostatic centrifugal spray system was designed for agricultural spraying drones,including a shell,a diversion shell,and an electrostatic ring.Then,experiments were conducted to optimize the setting of the main parameters that affect the charge-to-mass ratio,and outdoor spraying experiments were carried out on the spraying effect of the air-assisted electrostatic centrifugal spray system.The results showed the optimum parameters were that the centrifugal rotation speed was 10000 r/min,the spray pressure was 0.3 MPa,the fan rotation speed was 14000 r/min,and the electrostatic generator voltage was 9 kV;The optimum charge-to-mass ratio of the spray system was 2.59 mC/kg.The average deposition density of droplets on the collecting platform was 366.1 particles/cm^(2) on the upper layer,345.1 particles/cm^(2) on the middle layer,and 322.5 particles/cm^(2) on the lower layer.Compared to the results of uncharged droplets on the upper,middle,and lower layers,the average deposition density was increased by 34.9%,30.4%,and 30.2%,respectively,and the uniformity of the distribution of the droplets at different collection points was better.
基金The authors acknowledge that this study was financially supported by the National Key R&D Program of China(Grant No.2020YFD1000300No.2018YFD0200801)+1 种基金National ten thousand talents special support program of China[2018]no.29Innovation and Entrepreneurship Training Program of Hunan Agricultural University(Grant No.2019062x).
文摘Automatically identifying the degradability of municipal solid waste(MSW)is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes.In this study,a cost-effective method was proposed for the degradability identification of MSW.Firstly,the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site.Secondly,a lite convolutional neural network(CNN)model was built with only 3.37 million parameters,and then a total of eight models were trained on these datasets with and without the image augmentation operations,respectively.Finally,a degradability identification system was built for on-site composting,where the images were cut to different sizes of small squares for prediction,and the experiments were conducted to find the best combinations of the trained models and the cutting size.The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher,and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%,which indicated the capability of this cost-effective method to identify the degradability of MSW.
基金funded by the National Key Research and Development Program of China(Grant No.2018YFD0200800)Key Research and Development Program of Hunan Province(Grant No.2018GK2013)+1 种基金Hunan Modern Agricultural Industry Technology Program(Grant No.201926)Innovation and Entrepreneurship Training Program of Hunan Agricultural University(Grant No.2019062x).
文摘Fast assessment of the initial carbon to nitrogen ratio(C/N)of organic fraction of municipal solid waste(OFMSW)is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process.In this study,a novel approach was proposed to estimate the C/N of OFMSW,where an instance segmentation model was applied to predict the masks for the waste images.Then,by combining the instance segmentation model with the depth-camera-based volume calculation algorithm,the volumes occupied by each type of waste were obtained,therefore the C/N could be estimated based on the properties of each type of waste.First,an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks(Mask R-CNN)model.Second,a volume measurement algorithm was proposed,where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property.Then the calculated volume was corrected with linear regression models.The results showed that the trained instance segmentation model performed well with average precision scores AP_(50)=82.9,AP_(75)=72.5,and mask intersection over unit(Mask IoU)=45.1.A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R2=0.97 and root mean square error RMSE=0.10.The relative average error was 0.42%and the maximum error was only 1.71%,which indicated this approach has potential for practical applications.
