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 AP50 = 82.9, AP75 = 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. Keywords: carbon to nitrogen ratio, estimation, volume measurement, organic fraction of municipal solid waste, depth camera, instance segmentation DOI: 10.25165/j.ijabe.20211405.6382 Citation: Huang J J, Zhang H D, Xiao X, Huang J Q, Xie J X, Zhang L, et al. Method for C/N ratio estimation using Mask R-CNN and a depth camera for organic fraction of municipal solid wastes. Int J Agric & Biol Eng, 2021; 14(5): 222–229.
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. Keywords: strawberry detection, 3D point cloud, mean-shift, clustering method DOI: 10.25165/j.ijabe.20221506.7306 Citation: Hu H M, Kaizu Y, Zhang H D, Xu Y W, Imou K, Li M, et al. Recognition and localization of strawberries from 3D binocular cameras for a strawberry picking robot using coupled YOLO/Mask R-CNN. Int J Agric & Biol Eng, 2022; 15(6): 175–179.
This paper focuses on the problem of inserting constrained edge into D-triangulation.It is a very effective method of changing D-triangulation into CD-triangulation that constrained edge is inserted into D-triangulation,and only CD-triangulation can present real terrain and relief.It introduces some basic conceptions about the algorithm of inserting constrained edge,analyses present algorithms’ characteristics and presents a better algorithm of inserting constrained edge——inserting-swapping algorithm.The algorithm can effectively deal with all kinds of instances,and can be implemented by programs easily,and can accord with requirements of the project.
As there are significant variations of cell elasticity among individual cells, measuring the elasticity of batch cells is required for obtaining statistical results of cell elasticity. At present, the micropipette aspiration (MA) technique is the most widely used cell elasticity measurement method. Due to a lack of effective cell storage and delivery methods, the existing manual and robotic MA methods are only capable of measuring a single cell at a time, making the MA of batch cells low efficiency. To address this problem, we developed a robotic MA system capable of storing multiple cells with a feeder micropipette (FM), picking up cells one-by-one to measure their elasticity with a measurement micropipette (MM). This system involved the following key techniques: Maximum permissible tilt angle of MM and FM determination, automated cell adhesion detection and cell adhesion break, and automated cell aspiration. The experimental results demonstrated that our system was able to continuously measure more than 20 cells with a manipulation speed quadrupled in comparison to existing methods. With the batch cell measurement ability, cell elasticity of pig ovum cultured in different environmental conditions was measured to find optimized culturing protocols for oocyte maturation.
In plant protection, the increasing maturity of unmanned aerial vehicle (UAV) technology has greatly increased efficiency. UAVs can adapt to multiple terrains and do not require specific take-off platforms. They do well, especially in farmland areas with rugged terrain. However, due to the complex flow field at the bottom of a UAV, some of the droplets will not reach the surface of a plant, which causes pesticide waste and environmental pollution. Droplet deposition is a good indicator of the utilization rate of pesticides; therefore, this review describes recent studies on droplet deposition for further method improvement. First, this review introduces the flight altitude, speed, and environmental factors that affect pesticide utilization efficiency and then summarizes methods to improve pesticide utilization efficiency from three aspects: nozzles, electrostatic sprays, and variable spray systems. We also point out the possible direction of algorithm development for a UAV’s precision spray.
A Delaunay triangulation algorithm based on optimal convex hull technology is presented.The algorithm makes the discrete points sort in scan manner,and secondly it constructs convex hull and triangulates the sorted points by the optimal convex hull technology which is proved by the author,and optimizes triangles utilizing topological structures of directed edges.The algorithm avoids the test of point of intersection.Moreover, the average test times of a newly added point is under 4,so that the high efficiency of triangulation can be sure.
Coming with the age of digital mapping, traditional paper contour maps have been gradually displaced by digital contour maps, and the forming and processing of digital contours have been a focus in GIS. But adding annotation automatically to digital contours friendlily is a problem that has not been resolved well. By studying the location and density of annotations based on the people's habit of reading maps, this paper presents a new algorithm of adding elevation annotation automatically. Experiments prove that the result of the algorithm has satisfied the people's habit of reading maps.
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. Keywords: municipal solid waste, degradability identification, cost-effective, CNN, on-site composting, image classification DOI: 10.25165/j.ijabe.20211404.5838 Citation: Huang J J, Dai S H, Hu H M, Zhang H D, Xie J X, Li M. Cost-effective method for degradability identification of MSW using convolutional neural network for on-site composting. Int J Agric & Biol Eng, 2021; 14(4): 233–237.
Here, the improved multi-scale YOLO algorithm (Improved-YOLOv3) is presented, which was proposed to realize fast and accurate recognition of citrus fruit in a field environment. With the modification of the YOLO-styled network model, a darknet-53 backbone network with residual modules was designed. A multi-scale detection module was to construct a network model for rapid recognition of citrus fruit in complex environments. Using the improved model to detect and identify citrus fruit targets, the network model can extract more feature information. The improved YOLOv3 model was tested with citrus data, and the detection performance of the improved network and the influence of the number of backbone network layers on the feature extraction capability were compared. The results showed a good detection ability (detection rate, accuracy, map, detection speed) for the target fruit, and the improved YOLOv3 network showed higher accuracy. Moreover, the performance of different training models were compared: the Improved-YOLOv3 has stronger robustness, higher detection accuracy, and shorter training time, and can recognize citrus in complex field environments. The experiment showed that the precision of Improved-YOLOv3 was 90.5% and the accuracy reached 94.3%, the recall rate was 90.3%, the detection time was 9.89 ms per frame, which could provide technical support for this visual recognition system of citrus picking robot.
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