Manual Measurement Results Research Articles

Fully automated measurement of noise, signal-to-noise ratio, and contrast-to-noise ratio on chest CT images: feasibility and efficiency.

Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge. To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR. A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset. Based on the research dataset, automatically segment and measure the average CT values and standard deviation (SD) of CT values for background air and lung field under different thresholds to obtain noise, SNR, and CNR results. Using the results of manual measurements as the reference standard, we determine the optimal threshold with the highest consistency. Using internal and external test datasets, validate the consistency of automated measurements of noise, SNR, and CNR at the optimal CT threshold with reference standards. With background air set at -900 HU and lung field at -800 HU as thresholds, the automated measurements of noise, SNR, and CNR demonstrate the highest consistency with the reference standards. At the optimal threshold, the noise, SNR, and CNR measured automatically on both the internal (intraclass correlation coefficient [ICC] = 0.85-0.96) and external (ICC = 0.75-0.85) test datasets exhibit high consistency with their respective reference standards. The method we explored can intelligently measure the noise, SNR, and CNR of chest CT images, exhibits high consistency with radiologists, and offers a novel tool for image quality evaluation and analysis.

Cattle Body Size Measurement Based on DUOS-PointNet+.

The common non-contact, automatic body size measurement methods based on the whole livestock point cloud are complex and prone to errors. Therefore, a cattle body measuring system is proposed. The system includes a new algorithm called dynamic unbalanced octree grouping (DUOS), based on PointNet++, and an efficient method of body size measurement based on segmentation results. This system is suitable for livestock body feature sampling. The network divides the cow into seven parts, including the body and legs. Moreover, the key points of body size are located in the different parts. It combines density measurement, point cloud slicing, contour extraction, point cloud repair, etc. A total of 137 items of cattle data are collected. Compared with some of the other models, the DUOS algorithm improves the accuracy of the segmentation task and mean intersection by 0.53% and 1.21%, respectively. Moreover, compared with the manual measurement results, the relative errors of the experimental measurement results are as follows: withers height, 1.18%; hip height, 1.34%; body length, 2.52%; thoracic circumference, 2.12%; abdominal circumference, 2.26%; and cannon circumference, 2.78%. In summary, the model is proven to have a good segmentation effect on cattle bodies and is suitable for cattle body size measurement.

Study of Early Iron Age Vessels Using 3D-Modeling (Based on Baraba, Sargat Culture): Metric Characteristics

Purpose. The purpose of the paper is to approve and identify the possibilities of 3D modelling in the study of the Early Iron Age vessels.Methods. A series of experiments comparing computer and manual measurements of the vessels metric characteristics have been carried out (corolla diameter and vessel height). The experiments are based on 3D-models of three Early Iron Age vessels: a complete round-bottom vessel; a complete flat-bottom vessel; a fragmented round-bottom vessel. The respondents are represented by two groups: Iron/Bronze age and Stone Age researchers. The methods of statistics and arithmetic were used to process the results. The obtained computer and manual metric characteristics were subjected to arithmetic and statistical analyses.Results. It was revealed that the differences in measuring the corolla diameter between two groups were 0.1–1 mm. Measurements of vessels height by Stone Age researchers are more exact: the difference at the minimum discrepancies is less than 1 mm, the maximum values diverged up to 7 mm. According to the results of preliminary statistical analysis, there are no differences between the researcher groups on metric measurements of vessels. Statistical analysis showed that the results of manual measurements of simple objects (corollas) are sufficiently accurate and can be used for research. If a complex object (the complete vessel) is to be measured, it is better to use computerised measurements. Manipulation of high-precision artefact measurements is possible through the application of 3D-modelling.

Computer vision-based 3D coordinate acquisition of surface feature points of building structures

In the present structure health monitoring work, regular monitoring of the deformation and displacement of the target building structure is an important means to evaluate the safety of the structure. Aiming at the difficulties of traditional measurement methods such as complex operation, high cost and too few monitoring points, this paper proposes a method based on computer vision to obtain 3D coordinates of surface feature points of building structures. Based on the principle of restoring the target structure from the camera motion, the sparse point cloud model of the target building is initially reconstructed and the attitude information of each camera is obtained simultaneously. The depth information of each pixel in the original image is obtained by trial matching based on the principle of pole line search, and corrected and optimized based on the consistency of optics and geometry. Based on the original sparse point cloud, the depth information of each image was fused and reprojected based on the depth map registration principle to reconstruct the dense point cloud model of the target building. Finally, the 3D coordinates of the target building surface points are obtained and measured based on the scale factor method. In order to verify the feasibility of the method, seven buildings of different sizes were selected for field measurement and compared with manual measurement results. The results show that the measurement results of the proposed method are almost the same as those of manual measurement, and the error level fully meets the requirement of the error limit of the general structural health monitoring work. The method described in this paper obtains a large number of 3D coordinates of detection points on the surface of building structures in a non-contact form at one time, improves the flexibility and intensity of measurement point layout in the actual measurement work, and its operation is simple and low cost.

Automatic measurement of CT number in the ACR CT phantom and its implementation to investigate the impact of tube voltage on the measured CT number

This study aims to develop a software for measuring the computed tomography (CT) number of multiple materials automatically and investigate the impact of tube voltage on the measured CT numbers. The software was specifically design to automatically measure CT number of multiple materials on images of an ACR 464 CT phantom (Gammex, Inc.). Automatic measurements were performed using the matching technique based on the centroids of the phantom and bone objects. The software was tested first on images of the phantom scanned using a GE Revolution EVO 128-slice with rotation variations from 1° to 10° to evaluate its accuracy against rotation. The software was then implemented on images of the phantom scanned with tube voltage variations of 80, 100, 120, and 140 kV. Results of automatic measurements were compared with manual measurements. Manual measurements were performed using a MicroDicom Viewer (MicroDicom Ltd, Sofia, Bulgaria). The results of the two methods were analysed using a paired t-test. The developed software is able to accurately measure the CT number of multiple materials from images reconstructed with variations of rotation and tube voltage. The results of automatic method are not significantly different from the results of manual measurements with a p-value of >0.05. This study indicated that there is a dependency between the CT number values and the tube voltage. Thus, it is recommended in the quality control (QC) program to measure the CT number of multiple materials using all tube voltages available on the CT machine.

A method for obtaining maize phenotypic parameters based on improved QuickShift algorithm

Phenotype is an important bridge for studying the mechanism of “genotype-phenotype-environment”, and studying precise measurement of crop individual phenotypic information is of great significance for accelerating breeding processes and assisting in precision agriculture monitoring. The purpose of this study is to develop a clustering algorithm for corn population point clouds to accurately extract the three-dimensional morphology of individual crops. Firstly, this study used drones to obtain field corn data, reconstructed corn plant models based on the SfM algorithm, simplified and rotated point clouds, and extracted crop point clouds using methods based on color and distance thresholds. For density segmentation, this study improved the QuickShift method to segment individual corn crops. Color weights were added to the clustering density calculation process, and the original point cloud was projected onto a horizontal plane to calculate the density clustering core. Then, the remaining points were subjected to QuickShift clustering segmentation in 3D to obtain the point cloud data of individual corn plants. The use of cylindrical fitting and conditional Euclidean clustering algorithms to automatically segment point clouds of stems and leaves has achieved estimation of plant height, stem thickness, minimum bounding box volume per plant, and number of leaves. Finally, the true and measured values of plant height and stem thickness were compared. The results showed that the accuracy of the improved QuickShift method for segmentation was 93.10%, with an average measured plant height of 14.21 cm and an average stem diameter of 0.72 cm. The determination coefficient and root mean square error of the automatic and manual measurement results of plant height are 0.9186 and 0.5785 cm, respectively. The determination coefficients and root mean square errors of the automatic and manual measurement results of stem thickness are 0.8227 and 0.0574 cm, respectively. This study uses an improved QuickShift to cluster and segment corn point cloud data, achieving the segmentation of individual corn plants in the field, providing an automated, efficient, and low-cost solution for accurately measuring crop phenotypic information.

Unmanned aerial vehicle implementation for pavement condition survey

The Utilization of Unmanned Aerial Vehicle (UAV) as a vehicle for comprehensive and fast road condition data acquisition is expected to minimize or even replace conventional road condition surveys in the field. In addition, the final UAV product in the form of a 2D/3D model of road conditions has been proven to be used as a medium for interpreting road damage and measuring the dimensions of road damage without having to do it in the field. The significant difference between this study and other studies is found in the use of the Surface Distress Index and Pavement Condition Index methods for assessing road conditions on the 2D/3D model of the UAV results. No research compares the use of the two methods in 2D/3D models. This research took photos of pavement conditions using drones to produce 2D and 3D models as visual media for assessing pavement conditions using the Pavement Condition Index and Surface Distress Index methods. The results of the assessment on the model compared with the assessment in the field. In comparison, the dimension level of accuracy of pavement distress in the model against the results of manual measurements in the field, edge cracking has the lowest accuracy value of 75.72%, and joint reflective crack has the highest accuracy value of 97.86%. The linear regression equation for the Pavement Condition Index method in the model against manual in the field is y = 0.931x + 3.6003 with a coefficient of determination r2 = 0.86 and an MSE of 121.333, while the linear regression equation for the Surface Distress Index method in the model against manual in the field is y = 0.5831x + 28.867 with a coefficient of determination that is r2 = 0.653 and MSE = 2115. Based on the comparison above, in this study, the application of UAV for pavement condition assessment is more precise using the Pavement Condition Index method.

Measurement method of tear meniscus height based on deep learning.

Tear meniscus height (TMH) is an important reference parameter in the diagnosis of dry eye disease. However, most traditional methods of measuring TMH are manual or semi-automatic, which causes the measurement of TMH to be prone to the influence of subjective factors, time consuming, and laborious. To solve these problems, a segmentation algorithm based on deep learning and image processing was proposed to realize the automatic measurement of TMH. To accurately segment the tear meniscus region, the segmentation algorithm designed in this study is based on the DeepLabv3 architecture and combines the partial structure of the ResNet50, GoogleNet, and FCN networks for further improvements. A total of 305 ocular surface images were used in this study, which were divided into training and testing sets. The training set was used to train the network model, and the testing set was used to evaluate the model performance. In the experiment, for tear meniscus segmentation, the average intersection over union was 0.896, the dice coefficient was 0.884, and the sensitivity was 0.877. For the central ring of corneal projection ring segmentation, the average intersection over union was 0.932, the dice coefficient was 0.926, and the sensitivity was 0.947. According to the evaluation index comparison, the segmentation model used in this study was superior to the existing model. Finally, the measurement outcome of TMH of the testing set using the proposed method was compared with manual measurement results. All measurement results were directly compared via linear regression; the regression line was y0.98x-0.02, and the overall correlation coefficient was r 20.94. Thus, the proposed method for measuring TMH in this paper is highly consistent with manual measurement and can realize the automatic measurement of TMH and assist clinicians in the diagnosis of dry eye disease.

Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.

Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we propose a dynamic 3D data acquisition method in the field suitable for various crops by using a consumer-grade RGB-D camera installed on a ground-based movable platform, which can collect RGB images as well as depth images of crop canopy sequences dynamically. A scale-invariant feature transform (SIFT) operator was used to detect adjacent date frames acquired by the RGB-D camera to calculate the point cloud alignment coarse matching matrix and the displacement distance of adjacent images. The data frames used for point cloud matching were selected according to the calculated displacement distance. Then, the colored ICP (iterative closest point) algorithm was used to determine the fine matching matrix and generate point clouds of the crop row. The clustering method was applied to segment the point cloud of each plant from the crop row point cloud, and 3D phenotypic traits, including plant height, leaf area and projected area of individual plants, were measured. We compared the effects of LIDAR and image-based 3D reconstruction methods, and experiments were carried out on corn, tobacco, cottons and Bletilla striata in the seedling stage. The results show that the measurements of the plant height (R²= 0.9~0.96, RSME = 0.015~0.023 m), leaf area (R²= 0.8~0.86, RSME = 0.0011~0.0041 m 2 ) and projected area (R² = 0.96~0.99) have strong correlations with the manual measurement results. Additionally, 3D reconstruction results with different moving speeds and times throughout the day and in different scenes were also verified. The results show that the method can be applied to dynamic detection with a moving speed up to 0.6m/s and can achieve acceptable detection results in the daytime, as well as at night. Thus, the proposed method can improve the efficiency of individual crop 3D point cloud data extraction with acceptable accuracy, which is a feasible solution for crop seedling 3D phenotyping outdoors.

Static and Dynamic Performance Evaluation of a Solid-State LiDAR for 3D Object Detection in Greenhouse Spray Applications

Highlights Comprehensive evaluation of the measurement accuracy of an inexpensive solid-state LiDAR for object detection. Development of an algorithm to acquire point clouds of objects with various shapes under both static and dynamic conditions. Utilization of pseudo-color images to assess the surfaces of regular-shaped cartons and irregular artificial plants. Proposal for integrating the solid-state LiDAR into variable-rate spray applications for greenhouses. Abstract. An effective variable-rate spraying system for greenhouses requires accurate canopy structure parameters of plants to ensure proper pesticide dosage adjustment. While conventional laser systems integrated into spray systems can provide precise point cloud data of plants, they still present a high expense. This study examines the performance of a recently introduced, cost-effective, and high-resolution solid-state LiDAR (Intel RealSense L515) in relation to its potential for greenhouse spray applications. Additionally, a specialized point cloud acquisition algorithm was developed for this solid-state LiDAR to obtain the geometrical parameters of objects. To assess the LiDAR sensor's suitability for greenhouse spray applications, the performance of the LiDAR sensor and the algorithm was evaluated using five different sized regular-shaped cartons and three artificial plants with complex geometry. Various factors were analyzed, such as the horizontal distances between objects and the LiDAR sensor (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m), the tilt angle of the LiDAR sensor relative to the ground (45°, 60°, and 75°), the height of the LiDAR sensor from the ground (ranging from 0.3 to 0.8 m with 0.5 m distance intervals), and the forward speed of the LiDAR sensor (0.1, 0.3, 0.6, and 0.9 m s-1). The findings revealed that the optimal detection distance for this LiDAR sensor is 1.0 m. Increasing or decreasing the detection distance of the object relative to the LiDAR sensor diminished the measurement accuracy. The accuracy of the derived geometrical variables was affected by the height and tilt angle of the LiDAR sensor. Nevertheless, the geometrical parameters obtained from the solid-state LiDAR showed a favorable correspondence with the results of manual measurements. The highest root mean square error (RMSE) and coefficient of variation (CV) for the overall test were 14.3 mm and 14.3% in the X (length) direction, 14.3 mm and 14.3% in the Y (width) direction, and 10.8 mm and 10.8% in the Z (height) direction, respectively. The contour Edge Similarity Score for objects measured using the solid-state LiDAR and images obtained with an RGB camera exceeded 0.90. These findings suggest that the proposed solid-state LiDAR and the specifically designed algorithm could be effectively adapted to acquire the geometrical parameters of objects and to develop precise variable-rate spraying systems for greenhouse applications. Keywords: Canopy structure measurements, Point cloud, Precision agriculture, Precision spray technology, Variable-rate spraying systems.

Research on a Real-Time Monitoring Method for the Three-Dimensional Straightness of a Scraper Conveyor Based on Binocular Vision

Measuring the straightness of the scraper conveyor, which is an indispensable piece of equipment in a fully mechanized coal face, can prevent accidents such as derailment of the shearer and is also important for the precise positioning of the shearer and the accurate control of the hydraulic support. The existing scraper conveyor straightness measurement methods have the disadvantages of inconsistent measurement cost, accuracy, and reliability as well as low dimension of straightness description. To this end, this paper proposes a method for monitoring the three-dimensional straightness of a scraper conveyor based on binocular vision. Trapezoidal window matching technology was used to realize the image acquisition of multiple stations and multiple sensors. The bit pose acquisition model of binocular vision 3D reconstruction was used to obtain the 3D coordinates of the feature points on the sign board in each local coordinate system and the bit pose information of each sign board. The pose relay videometric method was used to convert the straightness in each local coordinate system to the global coordinate system to realize the 3D reconstruction of the scraper conveyor. Finally, the straightness measurement test of the preset scraper conveyor was carried out, and the error analysis was carried out by comparing with the manual measurement results, which showed that the measurement errors were all within ±30 mm. At the same time, the standard deviation of errors in both directions was small, which indicates that the straightness measurement method for the scraper conveyor in this paper has high reliability. In addition, the visual measurement process is independent of each other, so there is no error accumulation in the visual straightness measurement method. The analysis shows that the method of measuring the straightness of a scraper conveyor based on binocular vision can realize continuous and real-time monitoring of the scraper conveyor.

Precision control of polyurethane filament drafting and winding based on machine vision.

In the biomedical field, polyurethane (PU) is widely used in interventional catheters, artificial hearts, artificial blood vessels, orthopedic materials, medical adhesives, and other medical devices. In this paper, a method based on machine vision was proposed to control the drafting and winding accuracy of PU filament in order to solve the problem of centrifugal runout when the mold rotates. The centrifugal runout of the mold directly affected the preparation efficiency and quality of long artificial blood vessel by wet spinning. Through non-contact real-time detection of the filament diameter and the angle between the axis of filament and the axis of mold, the motion parameters of the two motors driving the moving platform and the drafting roller could be adjusted in real time to achieve the purpose of online real-time control of filament drafting and winding accuracy. The vision control method proposed in this paper was used to carry out the PU tube preparation experiment. The visual measurement results of the filament diameter and the included angle were compared with the manual measurement results. The average value of the diameter error is 0.0096mm, and the average value of winding angle is 0.4777°. The results proved the accuracy of the visual measuring method and testified it feasible to using machine vision instead of manual method to detect filament diameter and winding angle. Properties of the prepared PU tube were tested and analyzed. The filament diameter measured by the 3D microscope was about 0.87 mm and significantly smaller than the filament diameter before winding. This indicated that the winding was uniform, the extrusion was tight, and the adhesion was good.

Research on a pavement pothole extraction method based on vehicle-borne continuous laser scanning point cloud

Automatic detection and rapid extraction of condition characteristics of pavement potholes provide important support for road health monitoring and maintenance. In this paper, a method of extracting pavement potholes in vehicle-borne continuous laser point cloud combined with two-dimensional and three-dimensional analysis is proposed. This method mainly includes three parts: Firstly, point cloud data preprocessing is carried out to remove non-road point cloud and road noise points; then the transverse and longitudinal sections of the road are extracted according to the scanning line index of the laser point cloud. The Douglas–Pucker operator is used to analyse the profile contour characteristics, and the integral invariant operator is used to identify the initial feature points of the pothole; finally, the local datum surface of the pavement is constructed by fitting, and the continuous depth image of the pavement is generated. The active contour model operator is used to accurately extract the pothole boundary based on the local depth information, and geometric information such as the depth and area of the pothole is obtained. Selecting some road point cloud data for experiments, the results show that the pothole feature point extraction method in this paper is relatively robust, the extraction precision rate reaches 86.1%, and the recall rate reaches 91.2%. By generating a local depth image for precise extraction of the pothole boundary, it can effectively solve the problem of inaccurate extraction of the pothole boundary based on discrete point clouds. Compared with the manual measurement results, the relative error of the area is less than 7%, and the relative error of the depth is less than 6%, which proves the effectiveness of this method.

Design of Intravenous Fluid Monitoring in Inpatients Based on Internet of Things (IoT)

Infusion is a medical device that is used to replace lost body fluids or nutrients in some emergency or urgent situations, such as in patients who are dehydrated, unable to digest food by mouth, replaced by intravenous fluids, as for some disorders that usually occur such as blockage of fluids that cause effects. The type of research used is quantitative research with experimental methods. Methods of data collection using the method of observation, interviews, and literature study. The data source of this research uses the Research Library. The data processing methods of this research are reduction and coding. The analysis used is quantitative data analysis. The system built can make it easier for medical users to control infusion fluids in real time (24 hours). The results of this study are sending data every 10-12 seconds, and auto refresh on the blynk application every 5 seconds with a measurement error percentage of 2.25 from the results of manual measurements and sending notifications to smartphones if the volume of infusion fluid is below 6 MM as much as 70x. So that in this prototype the data sharing process was successfully carried out because the ultrasonic data and the temperature of Mlx90614 for infusion fluids were successfully stored in the Blynk database via Arduino Wemos D1R2.

Automatic and Accurate Acquisition of Stem-Related Phenotypes of Mature Soybean Based on Deep Learning and Directed Search Algorithms.

The stem-related phenotype of mature stage soybean is important in soybean material selection. How to improve on traditional manual methods and obtain the stem-related phenotype of soybean more quickly and accurately is a problem faced by producers. With the development of smart agriculture, many scientists have explored soybean phenotypes and proposed new acquisition methods, but soybean mature stem-related phenotype studies are relatively scarce. In this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm. We subsequently obtained the pitch number, internodal length, branch number, branching angle, plant type spatial conformation, plant height, main stem length, and new phenotype-stem curvature. After 300 epochs, we compared the recognition results of various detection algorithms to select the best. Among them, YOLOX had a maximum average accuracy (mAP) of 94.36% for soybean stem nodes and scale markers. Through comparison of the phenotypic information extracted by the directed search algorithm with the manual measurement results, we obtained the Pearson correlation coefficients, R, of plant height, pitch number, internodal length, main stem length, stem curvature, and branching angle, which were 0.9904, 0.9853, 0.9861, 0.9925, 0.9084, and 0.9391, respectively. These results show that our algorithm can be used for robust measurements and counting of soybean phenotype information, which can reduce labor intensity, improve efficiency, and accelerate soybean breeding.

SOLOv2-based multi-view contactless bovine body size measurement

To address the problems that traditional bovine body measurement methods require a lot of manual assistance and lead to stress reactions in cattle, this paper achieves contactless measurement of bovine body length, withers height, chest breath, belly breath and chest depth by using a deep learning approach. This paper use SOLOv2 instance segmentation to identify cattle and extract cattle contours from the top and side views, combines cattle image dataset and OpenCV image processing function to extract cattle feature parts, and use discrete curvature calculation method to extract cattle body size to calculate feature points, and calculate cattle body size parameters by Euclidean distance calculation method. Experiments were conducted using custom model cattle to which bovine body size measurements were taken, after comparing with the manual measurement results, the average relative errors of body length, body height, chest depth, chest breath and belly breath of the model cattle were 1.36%, 0.44%, 2.05%, 2.80% and 1.47%, respectively. The experiment proved that this measurement method performed well in the non-contact measurement of bovine body size and had good accuracy, which provided a new way and method for the measurement of the non-stress response of cattle.

Aggregate Geometrical Features and Their Influence on the Surface Properties of Asphalt Pavement

Aggregate geometrical features directly affect asphalt pavement surface properties, which further affect the resistance to skidding of a road surface. In order to establish a relationship between the aggregate geometrical features and pavement surface properties, this paper employs an aggregate geometric characteristic evaluation system (AGCES) to describe the form property, angularity, and surface texture of aggregate particles. The geometrical feature parameters of 15 different aggregates were examined by AGCES and the corresponding surface properties of asphalt pavement prepared from the mentioned aggregates were evaluated by sand patch method, 2-Dimension Image-based Texture Analysis Method (2D-ITAM) and Walking Friction Tester (WFT), respectively. The relationships between the pavement surface property parameters and the aggregate geometric characteristic parameters studied were developed by the Levenberg-Marquarat and universal global optimization (LM-UGO). The results show that the calculated geometric characteristic parameters are in agreement with traditional manual measurement results. The pavement surface properties are significantly influenced by aggregate angularity and aggregate surface texture. Regression relationships were established to predict pavement surface properties from the aggregate geometrical features.

Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging

Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 s per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The best accuracy for indica and japonica identification was 99.950%, while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research.

Measurement of the banana pseudo-stem phenotypic parameters based on ellipse model

The measurement of banana pseudo-stem phenotypic parameters is a critical way to evaluate the growth status of bananas, and it can provide essential data support for mechanized cultivation operations such as fertilization and pesticide application. Existing studies mainly measure the diameter of banana pseudo-stem as its phenotypic parameter. The banana pseudo-stem cross section was closer to an ellipse other than a standard circle, so the diameter parameter cannot adequately represent the phenotypic characteristics of the banana plant. In this study, an automatic measuring device for banana pseudo-stem phenotypic parameters was developed. The device, which integrates three different types of sensors: a laser ranging sensor, a rotary encoder, and a digital camera, was used to obtain the point cloud and image data of banana pseudo-stem. A K-means point clouds clustering algorithm based on Euclidean distance was proposed. The point cloud of banana pseudo-stem was identified and extracted. A three-dimensional reconstruction algorithm based on the ellipse model was also proposed. The three-dimensional contour of the pseudo-stem was calculated to obtain three types of phenotypic parameters: the long axis length, the short axis length, and the perimeter. Further, a synchronous trigger image acquisition mechanism was used to take pictures of pseudo-stems during measurement. It can be utilized for manual assessment of the growth status of the banana. Field experimental results showed that the three banana phenotypic parameters had a high correlation with the manual measurement results, and R2 is always more significant than 0.95, the total average measurement error and relative error were only 6.16 mm and 4.38%, respectively, both are within the acceptable agronomy range. In general, this method has good universality for plant stem detection, and the stem phenotypic parameters can be obtained by means of a non-contact test, which is of great significance to the mechanized cultivation of the forest and fruit industry. Keywords: multi-sensor fusion, point cloud fitting, phenotypic parameter extraction, banana pseudo-stem, ellipse model DOI: 10.25165/j.ijabe.20221503.6614 Citation: Jiang Y L, Duan J L, Xu X, Ding Y H, Li Y, Yang Z. Measurement of the banana pseudo-stem phenotypic parameters based on ellipse model. Int J Agric & Biol Eng, 2022; 15(3): 195–202.

Automated measurement of Vickers hardness using image segmentation with neural networks

Vickers hardness is widely used in material performance testing due to its versatility and simple calculation. Traditional automated methods of measuring it are often easily affected by the unsatisfactory surface of the material, and yield large errors. For a more robust measurement of indentations that can yield results similar to those of manual measurements within an allowable range of error, this study proposes an automatic method to measure the Vickers indentation. It uses a convolutional neural network to segment the Vickers indentation from the background in images, and uses a bounding box to measure the length of the diagonal of the indentation. To train and verify the neural network, the authors constructed a dataset of images featuring indentations in samples composed of a variety of materials. Experiments were performed on standard hardness blocks, TiO2, copper, and nylon to compare the performance of the proposed method with the results of manual measurements. The results verify the robust performance of the proposed method on complex surfaces with deformable indentations. The code is available at https://github.com/SimonLeeCHN/IndentMes_code_ref.