Recognition and localization of strawberries from 3D binocular cameras for a strawberry picking robot using coupled YOLO/Mask R-CNN

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.

Cost-effective method for degradability identification of MSW using convolutional neural network for on-site composting

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.

Method for C/N ratio estimation using Mask R-CNN and a depth camera for organic fraction of municipal solid wastes

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.

Design and performance test of a novel UAV air-assisted electrostatic centrifugal spraying system

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.

Ultralow cross talk arrayed waveguide grating integrated with tunable microring filter array

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.