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A novel, yet simple, airborne microplastic (MP) sampling approach using global pollen monitoring equipment was applied to identify, characterise and quantify outdoor airborne MPs for the first time. Modification of Burkard spore trap tape adhesive provided particle capture and facilitated downstream spectroscopy analysis. 36 polymer types were identified from a total of 21 days sampling using Burkard spore traps at two locations (United Kingdom and South Africa). MPs were detected in 95 % of daily samples. Mean MP particle levels were 2.0 ± 0.9 MP m -3 (11 polymer types) in Hull (U.K.), during March, 2.9 ± 2.0 MP m -3 (16 types) in Hull in July, and 11.0 ± 5.7 MP m -3 (29 types) in Gqeberha, (S.A.) in August 2023. The most abundant polymer type was nylon (Gqeberha). The approach was compared with two passive sampling methods whereby 27 polymer types were identified and of these, 6 types were above the limit of quantification (LOQ), with poly(methacrolein:styrene) (PMA/PS) the most abundant. Irregularly shaped MPs < 100 µm in length were predominant from all sampling approaches. For the first time, airborne MPs were chemically characterised and quantified using volumetric pollen sampling equipment, representing a viable approach for future airborne MP monitoring., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Machine vision has demonstrated its usefulness in the livestock industry in terms of improving welfare in such areas as lameness detection and body condition scoring in dairy cattle. In this article, we present some promising results of applying state of the art object detection and classification techniques to insects, specifically Black Soldier Fly (BSF) and the domestic cricket, with the view of enabling automated processing for insect farming. We also present the low-cost "Insecto" Internet of Things (IoT) device, which provides environmental condition monitoring for temperature, humidity, CO 2 , air pressure, and volatile organic compound levels together with high resolution image capture. We show that we are able to accurately count and measure size of BSF larvae and also classify the sex of domestic crickets by detecting the presence of the ovipositor. These early results point to future work for enabling automation in the selection of desirable phenotypes for subsequent generations and for providing early alerts should environmental conditions deviate from desired values., Competing Interests: FT was employed by the company SciFlair Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hansen, Oparaeke, Gallagher, Karimi, Tariq and Smith.)

Background: Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS)., Results: We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set., Conclusions: PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap., (© The Author(s) 2019. Published by Oxford University Press.)

Leaf venation extraction studies have been strongly discouraged by considerable challenges posed by venation architectures that are complex, diverse and subtle. Additionally, unpredictable local leaf curvatures, undesirable ambient illuminations, and abnormal conditions of leaves may coexist with other complications. While leaf venation extraction has high potential for assisting with plant phenotyping, speciation and modelling, its investigations to date have been confined to colour image acquisition and processing which are commonly confounded by the aforementioned biotic and abiotic variations. To bridge the gaps in this area, we have designed a 3D imaging system for leaf venation extraction, which can overcome dark or bright ambient illumination and can allow for 3D data reconstruction in high resolution. We further propose a novel leaf venation extraction algorithm that can obtain illumination-independent surface normal features by performing Photometric Stereo reconstruction as well as local shape measures by fusing the decoupled shape index and curvedness features. In addition, this algorithm can determine venation polarity - whether veins are raised above or recessed into a leaf. Tests on both sides of different leaf species with varied venation architectures show that the proposed method is accurate in extracting the primary, secondary and even tertiary veins. It also proves to be robust against leaf diseases which can cause dramatic changes in colour. The effectiveness of this algorithm in determining venation polarity is verified by it correctly recognising raised or recessed veins in nine different experiments.

Machine vision systems offer great potential for automating crop control, harvesting, fruit picking, and a range of other agricultural tasks. However, most of the reported research on machine vision in agriculture involves a 2D approach, where the utility of the resulting data is often limited by effects such as parallax, perspective, occlusion and changes in background light - particularly when operating in the field. The 3D approach to plant and crop analysis described in this paper offers potential to obviate many of these difficulties by utilising the richer information that 3D data can generate. The methodologies presented, such as four-light photometric stereo, also provide advanced functionalities, such as an ability to robustly recover 3D surface texture from plants at very high resolution. This offers potential for enabling, for example, reliable detection of the meristem (the part of the plant where growth can take place), to within a few mm, for directed weeding (with all the associated cost and ecological benefits) as well as offering new capabilities for plant phenotyping. The considerable challenges associated with robust and reliable utilisation of machine vision in the field are also considered and practical solutions are described. Two projects are used to illustrate the proposed approaches: a four-light photometric stereo apparatus able to recover plant textures at high-resolution (even in direct sunlight), and a 3D system able to measure potato sizes in-the-field to an accuracy of within 10%, for extended periods and in a range of environmental conditions. The potential benefits of the proposed 3D methods are discussed, both in terms of the advanced capabilities attainable and the widespread potential uptake facilitated by their low cost.

Purpose: The purpose of this study is to present an alternative method for static radiologic assessment of the wrist for midcarpal instability (ie, palmar intercalated segmental instability [PISI] and dorsal intercalated segmental instability [DISI]). The triangulation method uses 3 anatomic landmarks observed on the standard lateral x-ray of the wrist., Methods: A total of 125 normal lateral radiographs were measured to determine the normal range for the dorsal limb (DL) to palmar limb (PL) ratio. A 2-step process of performing triangulation is described. The first step is nonspecific screening of the radiograph and defines values greater than 1.0 as having a DISI deformity and values less than 0.5 as having a PISI deformity. The second step is used only for borderline values, which takes the position of the wrist into consideration and uses a normagram (reference chart) to match the DL:PL ratio with the radiometacarpal (RM) angle., Results: The average lateral wrist position was 8.4 degrees of extension (-8.4). The average DL:PL ratio was 0.75 +/- 0.09 (range, 0.93-0.57)., Conclusions: Based on these data we defined DISI deformity of the wrist as DL:PL ratios greater than 1.0, and ratios less than 0.5 representing PISI deformities. The triangulation method of assessing midcarpal alignment of the carpus is a practical and simple alternative to the traditional static radiologic method of assessing midcarpal instability of the wrist.