Development of Artificial Intelligence Climate Chamber and Experimental Study on Crop Phenotype Acquisition

Background: The deficiencies of traditional artificial climate chambers in phenotypic collection and analysis were improved to achieve the high-throughput acquisition of crop phenotypes during the growth period. This paper has developed an artificial intelligence climate cabin with functions of crop cultivation management and phenotype acquisition during the whole growth period. This research also established an environmental control system, a crop phenotype monitoring system and a crop phenotype acquisition system with environmental parameter adjustment and crop image collection. Phenotypic feature extraction and other functions were carried out in the cultivation experiment, and phenotype acquisition of wheat was performed under different nitrogen fertiliser application rates. Comparison and analyses were performed by the systematic and manual measurement values of crop phenotype characteristics, and the acquisition of wheat table was evaluated based on artificial intelligence climate cabin. The goodness of fit of the model was used to classify data.Results: During the different growth periods of wheat, the correlation analysis between the systematic and manual measurement values of its leaf area, plant height and canopy temperature showed that the obtained correlation coefficient r was greater than 1, and the fitting determination coefficient R2 was greater than 0.7156, with errors. The coefficient root mean square error was less than 2.42, indicating that the two were positively correlated, and their correlation was excellent. Conclusion: The results verified the feasibility and applicability of the artificial intelligence climate cabin to study the phenotypic characteristics of crops.

Video-based quantification of human movement frequency using pose estimation: A pilot study

Assessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1–4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearson’s correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations (all r>0.99) with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, quantitative, video-based approach to assessment of repetitive movements in clinical populations.

A Comparative Structural Analysis of Four Radiosonde Models

In this study, we first performed a comprehensive structural analysis of four models of radiosondes (devices intended for use as the meteorological probe of a sounding balloon) manufactured by three different companies – Graw, Vaisala and Meteomodem. The radiosondes were disassembled for visual inspection and manual measurement, three-dimensional computed tomography images were taken of their inner structure, and the outer shapes of the radiosondes were scanned with a structured-light three-dimensional scanner. The structural properties of the radiosondes thus identified were then compared to one other, based on which the Meteomodem M10 was ranked as the least harmful in a potential collision. Next, the Meteomodem M10 radiosonde was used in collision tests with a heavy target and with a pumpkin model, in order to evaluate the possible damage caused by and to the radiosonde in different types of collisions.

Quantification of Root-Knot Nematode Infestation in Tomato Using Digital Image Analysis

Tomato is the most popular vegetable globally. However, in certain conditions, the vegetable is susceptible to plant parasites such as the root-knot nematode (RKN; Meloidogyne spp.). A proper detection method is required to identify RKN and eliminate related diseases. The traditional manual quantification of RKN using a microscope is a time-consuming and laborious task. This study aims to develop a semi-automated method to discern and quantify RKN based on size using an image analysis method. The length of RKN was assessed using three novel approaches: contour arc (CA), thin structure (TS), and skeleton graph (SG) methods. These lengths were compared with the manual measurement of RKN length. The study showed that the RKN length obtained by manual measurement was highly correlated to the length based on this method, with R2 of 0.898, 0.875, and 0.898 for the CA, TS, and SG methods, respectively. These approaches were further tested to detect RKN on 517 images. The manual and automated counting comparison revealed a coefficient of determination R2 = 0.857, 0.835 and 0.828 for CA, TS, and SG methods, respectively. The one-way ANOVA test on counting revealed F-statistic = 4.440 and p-value = 0.004. The ratio of length to width was investigated further at different ranges. The optimal result was found to occur at ratio range between 10–35. The CA, TS, and SG methods attained the highest R2 of 0.965, 0.958, and 0.973, respectively. This study found that the SG method is most suitable for detecting and counting RKN. This method can be applied to detect RKN or other nematodes on severely infected crops and root vegetables, including sweet potato and ginger. The study significantly helps in quantifying pests for rapid farm management and thus minimise crop and vegetable losses.

EVALUATION OF AN ELECTRONIC PERIODONTAL PROBE VERSUS A MANUAL PROBE IN THE PERIODONTAL DIAGNOSIS OF CHILDREN AGED 12-14 YEARS

Objective: The purpose of the trial is to comparatively analyze an electronic, pressure-calibrated probe third generation Parometer (Orange) and a standard, manual measurement probe WHO 621 (C type) in the context of taking periodontal variables when assessing periodontal status in childhood. Materials and methods: The subject of the study were 28 children aged between 12 and 14 years (12 boys and 16 girls). All patients were clinically examined, and the data were recorded on a specially prepared card. The recorded clinical variables contain: Assessment of oral hygiene habits (type of toothbrush, frequency of brushing); OHI as per Green Vermillion; Registration of dental status; Depth of gingival sulcus (on all teeth) with both types of probes; BOP (bleeding on probing), percentage of bleeding units with both types of probes; Taking into account the complete time needed to take the findings and the sensation of pain experienced by a digital rank scale during probing. Results and conclusion: The average depth of gingival sulcus measured with a mechanical periodontal probe was 1.62 mm, and with an electronic one - 1.38 mm (p <0.05). Values ​​for BOP with both types of probes showed an average of 0.30 ± 0.29, which is 30% of all bleeding units examined (p> 0.05). The time for recording the periodontal indices with both probes is, on average, 10 minutes. In both probes, the discomfort of about grade 4 was observed according to the ranking scale used to read sensitivity (p <0.05). There is a more pronounced sensitivity when using an electronic periodontal probe.

Application of Photogrammetry Technique for quarry stockpile estimation

The quarrying activities is one of the largest industries in the world which supplied aggregate primarily for construction of any buildings and structures. Continuous supply of aggregates is very important to ensure the construction activities can be carried out without delays. Hence, the quarry operators consistently monitor their stockpile volume to meet the client’s demands. In most cases, the determination of available stockpile at the quarry are done by utilizing conventional method (manual measurement of the stockpile’s dimension). This approach is time consuming and sometimes required professional surveyor to carry out the task. Hence in this work, a comparative study between conventional and photogrammetry method was done to estimate the stockpile in a quarry. Drone was flying to capture the aerial images of a stockpile in the quarry. The effect of the flying height and the percentage of overlapping on the accuracy of stockpile volume was studied. Result shows at lower percent of side overlap (50%), the accuracy of estimation is better. The difference between the photogrammetry technique and conventional method only 2.5%. It can be concluded that photogrammetry technique is very reliable to be applied by the quarry operators to estimate their stockpile volume.

Investigating the Benefits of Using 3D Camera Technology to Improve Wound Measurements in a Tissue Viability Service: Outcomes of a Pilot Implementation Study

Sub-optimal experience and outcomes for people with stalled wounds is common. Clinicians have limited methods for reliably and accurately measure wounds. Depth measurement is an important indicator of healing, and digital methods of imaging the wound may offer increased accuracy and enable clinical decision-making. This study aimed to implement a Panasonic FZ-M1 toughpad with WoundCareLite software version 1.5.0.0, to enable three-dimensional measurements in Tissue Viability (TV) service. Length, width, and depth measurement were compared with usual manual measurement using a paper ruler alongside a 2D photographic image. Statistical analysis included the comparison of wound dimension measures and a presentation of visual healing trajectories over 4 weeks using run-charts. 30 patients were recruited over five weeks (13 female and 17 male), representing 4% of the usual caseload. Manual measurement and 3D software automatic method demonstrated that the width and depth 3D auto measures were more accurate than manual measures but depth measures were often wrong thus making volumetric measures inaccurate. Consistent wound size measurement was feasible, and healing trajectories provide a useful means of continuous assessment. Technology guided measurement has potential benefits over manual measurement as a means of more accurately monitoring healing. In this case, depth measurement could not be accurately assessed in practice and further software innovation is indicated to enable outcome measurement in tissue viability services.

Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement

The study of microstructures for the accurate control of material properties is of industrial relevance. Identification and characterization of microstructural properties by manual measurement are often slow, labour intensive, and have a lack of repeatability. In the present work, the intermetallic phase ratio and grain size in the microstructure of known Mg-Sn-Al alloys were measured by computer vision (CV) technology. New Mg (Magnesium) alloys with different alloying element contents were selected as the work materials. Mg alloys (Mg-Al-Sn) were produced using the hot-pressing powder metallurgy technique. The alloys were sintered at 620 °C under 50 MPa pressure in an argon gas atmosphere. Scanning electron microscopy (SEM) images were taken for all the fabricated alloys (three alloys: Mg-7Al-5Sn, Mg-8Al-5Sn, Mg-9Al-5Sn). From the SEM images, the grain size was counted manually and automatically with the application of CV technology. The obtained results were evaluated by correcting automated grain counting procedures with manual measurements. The accuracy of the automated counting technique for determining the grain count exceeded 92% compared to the manual counting procedure. In addition, ASTM (American Society for Testing and Materials) grain sizes were accurately calculated (approximately 99% accuracy) according to the determined grain counts in the SEM images. Hence, a successful approach was proposed by calculating the ASTM grain sizes of each alloy with respect to manual and automated counting methods. The intermetallic phases (Mg17Al12 and Mg2Sn) were also detected by theoretical calculations and automated measurements. The accuracy of automated measurements for Mg17Al12 and Mg2Sn intermetallic phases were over 95% and 97%, respectively. The proposed automatic image processing technique can be used as a tool to track and analyse the grain and intermetallic phases of the microstructure of other alloys such as AZ31 and AZ91 magnesium alloys, aluminium, titanium, and Co alloys.

Enhanced geometric constraint-based phase unwrapping algorithm in binocular stereo vision fringe projection system

Phase unwrapping plays an important and central role in phase-based digital fringe projection profilometry. The unwrapping quality directly influences the three-dimensional measurement accuracy. Recently, an effective geometric constraint-based phase unwrapping algorithm has been proposed to obtain the continuous absolute phase map and the unwrapped phase accuracy was found to be high. However, in this technique the virtual depth plane at z = zmin is often created empirically, which increases the manual measurement error. For this reason, this paper proposes a method for accurately constructing the virtual plane and further applies it to phase unwrapping of objects with a larger depth range. In this method, a binocular stereo vision system is used as the measurement set-up for the virtual depth plane construction and a series of virtual depth planes at z = zimin (i ≥ 2) is automatically built using a computational framework. Then, the phase is unwrapped for each region according to the continuity of the unwrapped phase and a complete absolute phase map is obtained by merging the unwrapped phases in all regions for 3D reconstruction. In this process, the virtual depth planes are created automatically and quantitatively by the measurement system. No human intervention is required and it greatly reduces the manual measurement error. Experiments show that the artificial virtual planes can be built accurately and the phase is unwrapped correctly and readily.

Reconstruction of Conifer Root Systems Mapped with Point Cloud Data Obtained by 3D Laser Scanning Compared with Manual Measurement

Three-dimensional (3D) root system architecture (RSA) is a predominant factor in anchorage failure in trees. Only a few studies have used 3D laser scanners to evaluate RSA, but they do not check the accuracy of measurements. 3D laser scanners can quickly obtain RSA data, but the data are collected as a point cloud with a large number of points representing surfaces. The point cloud data must be converted into a set of interconnected axes and segments to compute the root system traits. The purposes of this study were: (i) to propose a new method for easily obtaining root point data as 3D coordinates and root diameters from point cloud data acquired by 3D laser scanner measurement; and (ii) to compare the accuracy of the data from main roots with intensive manual measurement. We scanned the excavated root systems of two Pinus thunbergii Parl. trees using a 3D laser scanner and neuTube software, which was developed for reconstructing the neuronal structure, to convert the point cloud data into root point data for reconstructing RSA. The reconstruction and traits of the RSA calculated from point cloud data were similar in accuracy to intensive manual measurements. Roots larger than 7 mm in diameter were accurately measured by the 3D laser scanner measurement. In the proposed method, the root point data were connected as a frustum of cones, so the reconstructed RSAs were simpler than the 3D root surfaces. However, the frustum of cones still showed the main coarse root segments correctly. We concluded that the proposed method could be applied to reconstruct the RSA and calculate traits using point cloud data of the root system, on the condition that it was possible to model both the stump and ovality of root sections.