In TIN of digital elevation model constructed from contours,flat areas which are made up of flat triangles can not reflect the real shape of the surface,so that they need be corrected properly.A corrective algorithm of flat areas was presented by the method.The TIN which flat areas were corrected could simulate the real shape of the surface to the fullest extent and could improve the speed of correcting flat areas,in which the inner flat triangles are divided of having no use of terrain characteristic lines.The time complexity of the algorithm is O(n).
In order to detect kiwifruit quickly and accurately in orchard environments for the picking robot, this paper proposed a detection method based on a lightweight YOLOv4-GhostNet network. The implementations of the method are as follows: The original CSP-Darknet53 backbone network model was replaced by GhostNet, a feature layer facilitating small object detection was introduced in the feature fusion layer, and part of the ordinary convolution was replaced by a combination of 1 × 1 convolution and depth-separable convolution to reduce the computational pressure caused by the fused feature layer. The parameters of the new network are reduced, and the generalization ability of the model is improved by loading pre-training weights and freezing some layers. The trained model was tested, and the results showed that the detection performances were better than that of the original YOLOv4 network. The F1 value, map, and precision were improved on the test set, which were 92%, 93.07%, and 90.62%, respectively. The size of weight parameters was reduced to 1/6 of the original YOLOv4 network, and the detection speed reached 53 FPS. Therefore, the method proposed in this study shows the features of fast recognition, lightweight parameters, and high recognition accuracy, which can provide technical support for vision systems of kiwifruit picking robots.
The zona pellucida (ZP) is an important component of the oocyte. The stiffness of ZP plays crucial roles in many applications, such as nuclear transplantations (NT), embryo micro-injections, and clone. While conventional methods usually evaluate the deformability of the oocytes assuming the oocytes in static equilibrium when deforming, a novel evaluation obtained from the dynamic deformation is utilized in this work. In this paper, a method based on the subpixel cell contour detection algorithm for evaluating the zona pellucida of the porcine oocytes' deformability is proposed. A subpixel cell contour detection algorithm is used to detect the edge of the porcine oocytes to get the deformation dynamic curve; a self-developed pneumatic micro-injection system is used to apply pressure on the oocyte to deform the oocyte, and a transfer junction is got to evaluate the deformability of the porcine oocytes. The R 2 of the fitted model is 0.9477.
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.
To improve the accuracy and reliability of orchard spraying robots, an integrated navigation system was developed, consisting of a real-time kinematic positioning-Beidou satellite navigation system (RTK-BDS) receiver, an inertial measurement unit (IMU), a navigation controller, and servo motors. Using the loose coupling combination method, an error Kalman filter algorithm based on the measurement of position and heading angle is implemented to correct the error of the inertial measurement unit in real time. Combining the kinematics model and the pure pursuit model of the spraying robot, a path-tracking control algorithm is proposed. Path planning was conducted according to the terrain characteristics of orchards. Field experiments were carried out on a spraying robot to evaluate the proposed auto-navigation system. The results showed that when the spraying robot was static, the positioning performances of BDS alone and that of the BDS/IMU combined system were similar, the positioning error was less than 1.5 cm, and the heading angle errors were within 0.3°; when the spraying robot moving alone to a straight line at the speed of 0.4 m/s, the position error of the navigation system only using BDS was less than 5.29 cm, the heading angle error was within 3°, while the position error of BDS/IMU integrated navigation system was less than 2.49 cm, and the heading angle error was within 2°. The accuracy of BDS/IMU integrated navigation system is significantly improved. When the orchard spraying robot was moving at the speed of 0.4 m/s, the maximum offset error was lower than 10.77 cm, the average offset error was not higher than 3.55 cm, and the root mean square error (RMSE) of the lateral deviation was 1.19 cm. The results showed that the proposed auto-navigation system could make the spraying robot track the pre-set path smoothly and stably.
Sweetness or sugar content, represented by soluble solids contents (SSC), is a vital quality trait in watermelon breeding which can be assessed by the refractive index method. However, sampling watermelon juice out of the pulp is a process that is both labor-intensive and error-prone. In this study, we developed an automatic SSC measurement system for watermelon breeding to improve efficiency and decrease costs. First, we built an automatic cutting system to cut watermelons into precise halves, in which a laser rangefinder is used to measure the distance from the upper surface of the watermelon to itself, and thus, the diameter is estimated. The experiments showed a high correlation between the estimated diameters and the ground truths, with and . Then, we built an automatic Brix measurement system to obtain the Brix data from a central point on the watermelon’s section, where an image analysis procedure is applied to locate the testing point. This is then transformed to the camera coordination system, and a refractometer is driven by a 3-axis robotic arm to reach the testing point. Brix measurement experiments were conducted using three vertical gaps and four lateral gaps between the probe of the refractometer and the pulp. The result showed that the best parameters were a vertical gap of 4 mm and a lateral gap of 2 mm. The average accuracy reached 98.74%, which indicates that this study has the potential to support watermelon breeding research.
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 10 000 r/min, the spray pressure was 0.3 MPa, the fan rotation speed was 14 000 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/cm2 on the upper layer, 345.1 particles/cm2 on the middle layer, and 322.5 particles/cm2 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. Keywords: UAV spraying, droplet drift, centrifugal sprayer, air-assisted spraying, electrostatic spraying DOI: 10.25165/j.ijabe.20221505.6891 Citation: Hu H M, Kaizu Y, Huang J J, Furuhashi K, Zhang H D, Xiao X, et al. Design and performance test of a novel UAV air-assisted electrostatic centrifugal spraying system. Int J Agric & Biol Eng, 2022; 15(5): 34–40.
In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better.
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.
Since the first cloned sheep was produced in 1996, cloning has attracted considerable attention because of its great potential in animal breeding. Somatic cell nuclear transfer (SCNT) is widely used for creating clones. However, SCNT is very complicated to manipulate and inevitably causes intracellular damage during manipulation. Typically, only less than 1% of reconstructed embryos develop into live cloned animals. This low success rate is considered to be the major limitation in the extensive application of cloning techniques. In this study, we proposed an intracellular strain evaluation-based oocyte enucleation method to reduce potential intracellular damage in SCNT. We first calculated the intracellular strain based on the intracellular velocity field and then used the intracellular strain as a criterion to improve the enucleation operation. We then developed a robotic batch SCNT system to apply this micromanipulation method to animal cloning. Experimental results showed that we increased the blastocyst rate from 10.0% to 20.8%, and we successfully produced 17 cloned piglets by robotic SCNT for the first time. The success rate of cloning was significantly increased compared to that of traditional methods (2.5% vs 0.73% on average). In addition to the cloning technique, the intracellular strain evaluation-based enucleation method is expected to be applicable to other biological operations and for establishing a universal cell manipulation protocol to reduce intracellular damage.
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