Life Cycle Assessment of an Avocado: Grown in South Africa-Enjoyed in Europe.

Food production is known to have significant environmental impacts, with the main contributors residing in the farming and transportation life cycle phases. Of the various food products transported around the world, avocados have increasingly gained attention as a high-commodity superfood. Avocados require specific climatic and agricultural conditions for farming, with the most fertile land and conditions located outside Europe. Consequently, most avocados consumed in Europe are imported over vast geographical distances, with little information available to quantify the environmental impacts of this imported superfood. This paper aims to present the most detailed life cycle assessment results of an avocado cultivated, grown and harvested in the Limpopo Province of South Africa and exported to the European market for sale and consumption. A life cycle assessment was developed for the farming, harvesting, handling, packaging, ripening, transportation, and carbon sequestration potential of the avocado, and it was used to conduct a holistic life cycle assessment. Input data was obtained through an 18-month data collection campaign across the relevant stakeholders. A baseline 'business-as-usual' scenario is focused on throughout this study, and scope for optimisation is identified for each life cycle phase where applicable, accompanied by uncertainty analyses. Results show a total carbon input of 904.85 kg CO2e/tonne. Mitigating this, 521.88 kg CO2e/tonne is offset, resulting in a net carbon footprint of 382.97 kg CO2e/tonne with uncertainty ranges of -23.22 to +58.69 kg CO2e/tonne, normalised to 57.45 g CO2e/avocado grown in South Africa and sold in Europe. The environmental impacts of the avocado industry under consideration are largely mitigated by the "nature first" philosophy of the farming and logistics enterprises, which have made significant investments in reducing emissions. Sensitivity analyses indicate that implementing large-scale renewable energy, using alternative packaging instead of cardboard, and selling avocados unripened could further enable the farming enterprise to achieve Net Zero objectives. These measures could reduce baseline emissions from 382.97 kg CO2e/tonne to a theoretical -68.54 kg CO2e/tonne, representing a 117.9% decrease. Although this study does not quantify climate change impacts, qualitative analyses suggest that climate change will have a net negative effect on the avocado industry in South Africa. These regions, typically located in micro-climates, are projected to become wetter and warmer, adversely affecting crop phenology, pest control, road conditions, management complexity, farmer livelihoods, and food security. The study recommends large-scale implementation of the optimisation strategies identified to achieve Net Zero objectives and the development of proactive climate change mitigation strategies to enhance the resilience of avocado supply chains to future stressors. These insights are crucial for policymakers, industry stakeholders, and consumers aiming to promote sustainability in the avocado market.

MADV-DAQ: Multi-channel Arduino-based differential voltage data acquisition system for remote strain measurement applications.

Wind turbine power generation, both onshore and offshore, has gained significant popularity over the past few decades. However, the design of a turbine's foundation, capable of supporting a tall structure subject to large horizontal forces, remains challenging. Complex wind loading and intricate soil-structure interaction between the foundation and the supporting soil requires consideration. Although commercial structural health monitoring (SHM) systems provide several advantages, they remain cost prohibitive. This paper demonstrates the development, testing, fabrication, installation and validation of a low-cost, multi-channel, Arduino-based differential voltage data acquisition system (MADV-DAQ) suitable for remote, battery powered measurements of multiple Wheatstone bridge-based (strain) sensors. The instrumented wind turbine (120 m high, 3.45 MW generation capacity) forms part of a newly constructed onshore wind farm in South Africa. The developed MADV-DAQ system proved valuable in measuring strains associated with the wind turbine tower, quantifying the true magnitude of the loads being transferred to the underlying foundation. MADV-DAQ was designed to relay the real-time measurements to two, independent cloud platforms for aggregation, visualization and subsequent analysis. MADV-DAQ was purposefully designed as a universal data acquisition system, compatible with any Wheatstone bridge-based sensor design, including strain gauges, tensiometer and similar MEMS-based sensors.

A low-cost, mobile real-time kinematic geolocation service for engineering and research applications.

Centimetre accurate geolocation service is beneficial to a wide range of applications, ranging from sports engineering, civil infrastructure, autonomous vehicles, surveying to digitisation of historically significant structures. Previously, these features were confined to prohibitively expensive commercial hardware, requiring technical knowledge and experience to operate. Continued technological advancements have seen the miniaturisation of electronics and antennas, coupled with an increase in the number and performance of global navigation satellite systems (GNSS) by various nations and organisations, providing global signal coverage. This paper demonstrates a low-cost, mobile, real-time kinematic (RTK) geolocation service for engineering and research applications, fabricated from components readily available from commercial suppliers. This solution, consisting of a mobile RTK base station and RTK rover, provides centimetre-accuracy performance up to a distance of 15 km away from the base station. Correction data is transmitted over the internet using free and open software solutions. The small footprint of both the RTK base station and RTK rover, provides versatile applications even in remote locations. The performance of the geolocation service is validated using field experiments, comparing measurements against state-of-the-art photogrammetry, light detection and ranging (LiDAR) and digital level measurement technologies. The authors encourage the adoption of the RTK geolocation solution based on the calibrated results.

RailEnV-PASMVS: A perfectly accurate, synthetic, path-traced dataset featuring a virtual railway environment for multi-view stereopsis training and reconstruction applications.

A Perfectly Accurate, Synthetic dataset featuring a virtual railway EnVironment for Multi-View Stereopsis (RailEnV-PASMVS) is presented, consisting of 40 scenes and 79,800 renderings together with ground truth depth maps, extrinsic and intrinsic camera parameters, pseudo-geolocation metadata and binary segmentation masks of all the track components. Every scene is rendered from a set of 3 cameras, each positioned relative to the track for optimal 3D reconstruction of the rail profile. The set of cameras is translated across the 100 m length of tangent (straight) track to yield a total of 1995 camera views. Photorealistic lighting of each of the 40 scenes is achieved with the implementation of high-definition, high dynamic range (HDR) environmental textures. Additional variation is introduced in the form of camera focal lengths, camera location and rotation parameters and shader modifications for materials. Representative track geometry provides random and unique vertical alignment data for the rail profile for every scene. This primary, synthetic dataset is augmented by a smaller photograph collection consisting of 320 annotated photographs for improved semantic segmentation performance. The combination of diffuse and specular properties increases the ambiguity and complexity of the data distribution. RailEnV-PASMVS represents an application specific dataset for railway engineering, against the backdrop of existing datasets available in the field of computer vision, providing the precision required for novel research applications in the field of transportation engineering. The novelty of the RailEnV-PASMVS dataset is demonstrated with two use cases, resolving shortcomings of the existing PASMVS dataset.

TD-DAQ: A low-cost data acquisition system monitoring the unsaturated pore pressure regime in tailings dams.

Tailings dams are large, often self-contained, storage facilities of mine residue. On self-contained tailings dams the tailings material itself is used to raise the containment embankments holding newly deposited residue. To develop the necessary strength, it is essential that material must dry out sufficiently. Despite substantial advancements in the field of instrumentation, these parameters are rarely measured on tailings dams and their evolution over time is poorly understood. Understanding the role of pore water suction and water content evolution over time can benefit from the installation of sensors and data acquisition systems (DAQ) capable of continuously monitoring these parameters. Such monitoring remains difficult and expensive owing to the challenges of measuring negative water pressures and the often-remote locations and harsh operating environments typical of mining operations. This paper describes the development, testing and validation of a low-cost DAQ for the measurement of the unsaturated pore pressure regime in a platinum tailings dam located in the Limpopo province of South Africa. The Tailings Dam DAQ (referred to as TD-DAQ) is designed to measure the negative pore pressure, moisture content and temperature in fine-grained material over extended periods of time. These measurements are stored on the DAQ and transmitted in parallel using new wireless network communications technologies (Sigfox) suited to remote, battery powered applications. The successful deployment of the TD-DAQ presents a real-time, low-cost instrumentation solution to improve the efficiency of condition monitoring of tailings storage facilities, contributing to a reduction in the probability of failure events.

PASMVS: A perfectly accurate, synthetic, path-traced dataset featuring specular material properties for multi-view stereopsis training and reconstruction applications.

A Perfectly Accurate, Synthetic dataset for Multi-View Stereopsis (PASMVS) is presented, consisting of 400 scenes and 18,000 model renderings together with ground truth depth maps, camera intrinsic and extrinsic parameters, and binary segmentation masks. Every scene is rendered from 45 different camera views in a circular pattern, using Blender's path-tracing rendering engine. Every scene is composed from a unique combination of two camera focal lengths, four 3D models of varying geometrical complexity, five high definition, high dynamic range (HDR) environmental textures to replicate photorealistic lighting conditions and ten materials. The material properties are primarily specular, with a selection of more diffuse materials for reference. The combination of highly specular and diffuse material properties increases the reconstruction ambiguity and complexity for MVS reconstruction algorithms and pipelines, and more recently, state-of-the-art architectures based on neural network implementations. PASMVS serves as an addition to the wide spectrum of available image datasets employed in computer vision research, improving the precision required for novel research applications.

HRSBallast: A high-resolution dataset featuring scanned angular, semi-angular and rounded railway ballast.

A high-resolution dataset of digitally scanned railway ballast (HRSBallast) is presented, consisting of 108 individual, digitised samples. The ballast samples were sourced from both a railway test track section located on a heavy haul coal export line in South Africa and a local quarry. The sampled ballast from the field installation represent angular, semi-angular and rounded geometric features depending on the sampled location. The fresh ballast sourced from the quarry was iteratively tested using a hydraulic actuator in a small-scale box test which forms part of a separate research project. The ballast samples were scanned before and after every test to ascertain the changes in geometry, in addition to the loss of material due to attrition. The field and laboratory samples were digitised using a high-resolution, commercial (Visual Simultaneous Localization and Mapping) VSLAM-based scanner with a 40-micrometre accuracy. Samples which were fractured by the hydraulic actuator are also included in the dataset. HRSBallast serves as a reference dataset for granular media (GM) simulations utilizing DEM (discrete element method), degradation or wear modelling, digital assets for the creation of synthetic datasets for deep learning applications, embedded railway instrumentation and video games requiring high-resolution geometry.

smAvo and smaTo: A fruity odyssey of smart sensor platforms in Southern Africa.

Avocado and tomato production are key agricultural sectors for many economies including South Africa. The quality of avocadoes and tomatoes that reach consumers is directly dependent on the handling at the postharvest stage. The fruit undergoes an extensive journey, and subsequently stress, comprised of several steps, including harvesting, application of postharvest treatments, packaging and transportation by road and sea to reach the export markets. To date, the quantification of these external stresses on a discrete fruit has not been measured in detail, primarily due to a lack of compatible instrumentation. This paper demonstrates a low cost, stand-alone, open source data acquisition system, termed smAvo and smaTo, that can be introduced effortlessly to monitor agricultural processing facilities and transportation networks. The miniaturised Arduino sensor platform is enclosed within a waterproof enclosure and surrounded by a 3D printed shell manufactured from morphologically compatible materials. The software is customisable to the needs of the research project or individual transportation phases under study, providing both extensive environmental data and high-frequency tri-axis acceleration measurements that are crucial to understanding the dynamic processes that directly affect the final quality of the fruit. Four successful field trials demonstrate the fidelity of both the smAvo and smaTo platforms.

Fly-by-Pi: Open source closed-loop control for geotechnical centrifuge testing applications.

Geotechnical centrifuges are valuable instruments for physical modelling of complex geotechnical problems in a controlled laboratory setting. In comparison to full-scale testing, scaled models are cost effective to construct and instrument and, when tested in a geotechnical centrifuge at increased centrifugal accelerations, are capable of replicating full-scale stress-strain soil behaviour. Centrifuge modellers require specialised hardware and instruments capable of functioning under high accelerations. Such hardware is costly, nearly always purpose built, and often rely on commercial, closed-source data acquisition systems, hardware and control systems. This paper demonstrates a novel and versatile, low cost, open source logger and control system that works in parallel alongside existing centrifuge hardware. This solution, termed Fly-by-Pi, was developed using the Raspberry Pi microcomputer. The system provides closed-loop control of linear actuators with the ability to operate in either cyclic, monotonic, or static load- or displacement-control. The control mechanism can be reprogrammed according to experimental requirements, even during flight in the centrifuge. Three independent experiments are described which included the Fly-by-Pi controller as a key component in their operation. Based on the experience gained during these experiments, the authors encourage wide-spread adoption of open-sourced hardware solutions in extreme testing environments.