Volatile phenols (VPs) derived from smoke-exposed grapes are known to confer a smoky flavor to wine. Current methods for determination of VPs in grape berries either involve complex sample purification/derivatization steps or employ two analytical platforms for free and bound VP fractions. We report here a simple gas chromatography-tandem mass spectrometry (GC-MS/MS) method for quantification of both free and bound VPs in grapes, based on optimized (1) GC-MS/MS parameters, (2) an analyte extraction procedure, and (3) phenol glycoside hydrolysis conditions. Requiring neither sample cleanup nor a derivatization step, this method is sensitive (LOD ≤ 1 ng/g berries) and reproducible (RSD < 12% for repeated analyses) and is expected to significantly reduce the sample turnover time for smoke taint detection in vineyards.

Advances in measurement systems and technologies are being avidly taken up in perennial tree crop research and industry applications. However, there is a lack of a standard model to support streamlined management and integration of the data generated from advanced measurement systems used in tree crop research. Furthermore, the rapid expansion in the diversity and volumes of data is increasingly highlighting the requirement for a comprehensive data model and an ecosystem for efficient orchard management and decision-making. This research focuses on the design and implementation of a novel proof-of-concept data ecosystem that enables improved data storage, management, integration, processing, analysis, and usage. Contemporary technologies proliferating in other sectors but that have had limited adoption in agricultural research have been incorporated into the model. The core of the proposed solution is a service-oriented API-driven system coupled with a standard-based digital orchard model. Applying this solution in Agriculture Victoria’s Tatura tree crop research farm (the Tatura SmartFarm) has significantly reduced overheads in research data management, enhanced analysis, and improved data resolution. This is demonstrated by the preliminary results presented for in-orchard and postharvest data collection applications. The data ecosystem developed as part of this research also establishes a foundation for early fruit traceability across industry and research.

Agrivoltaics offer a sustainable solution to the growing competition between food and energy production. However, their adoption is often constrained by the design and operation challenges associated with optimising the complex trade-off between crop yield and photovoltaic (PV) output. Digital twins can mitigate these risks, yet most agricultural digital twins operate as fragmented digital shadows, lacking high-fidelity modelling, advanced simulation, and bidirectional control capabilities. This study presents a comprehensive, end-to-end digital twin framework to address these limitations. The framework integrates a high-resolution 3D orchard model, reconstructed via UAV photogrammetry, with a CesiumJS-based web interface linked to a modular IoT architecture built on Node-RED, Message Queuing Telemetry Transport (MQTT) protocol and InfluxDB for real-time monitoring and control. A PV simulation engine supports the design, simulation and optimisation of agrivoltaic systems. Bidirectional communication was validated through remote actuation of a physical solar tracker, demonstrating integration among the 3D environment, sensor data and control systems to achieve a closed-loop digital twin. Simulation analyses suggested that panel orientation and row spacing exert a dominant influence on crop-level light distribution. Simulation results demonstrated that a 90° azimuth configuration achieved the highest daily energy yield of 53.97 kWh but reduced peak crop-level irradiance to 205 W/m². In contrast, the baseline 0° configuration offered a balanced output of 40.86 kWh with a peak light availability of 338 W/m². The validated, interoperable digital twin architecture provides a reference model for the design, simulation, monitoring and control of an agrivoltaic system, reducing investment uncertainty and supporting sustainable food–energy co-production.

Traditional horticultural information systems lack fine-grained, transparent on-farm event traceability, often providing only high-level post-harvest summaries. These systems also fail to standardise and integrate diverse data sources, ensure data privacy, and scale effectively to meet the demands of modern agriculture. Concurrently, rising requirements for global environmental, social, and governance (ESG) compliance, notably Scope 3 emissions reporting, are driving the need for farm-level visibility. To address these gaps, this study proposes a novel traceability framework tailored to horticulture, leveraging global data standards. The system captures key on-farm events (e.g., irrigation, harvesting, and chemical applications) at varied resolutions, using decentralised identification, secure data-sharing protocols, and farmer-controlled access. Built on a progressive Web application with microservice-enabled cloud infrastructure, the platform integrates dynamic APIs and digital links to connect on-farm operations and external supply chains, resolving farm-level data bottlenecks. Initial testing on Victorian farms demonstrates its scalability potential. Pilot studies further validate its on-farm interoperability and support for sustainability claims through digitally verifiable credentials for an international horticultural export case study. The system also provides a tested baseline for integrating data to and from emerging technologies, such as farm robotics and digital twins, with potential for broader application across agricultural commodities.