BackgroundCassava (Manihot esculenta), yam (Dioscorea spp.) and sweet potato (Ipomoea batatas) are highly perishable after harvest, and active (forced-convection) solar drying can reduce losses with little external energy. Dryer performance is governed by coupled airflow, heat transfer and moisture transport, which computational fluid dynamics (CFD) can resolve in principle. This critical narrative review appraises how far CFD and complementary models currently support the design, validation and optimisation of active solar dryers for tuber crops.MethodsA documented, non-exhaustive search of Scopus, Web of Science Core Collection, ScienceDirect and Google Scholar (to 30 September 2026), supplemented by citation chaining, was used. Evidence was classified into four tiers: direct tuber CFD; tuber-specific non-CFD numerical models; tuber experimental and kinetic studies; and transferable CFD or multiphysics studies of solar dryers for other products. Studies were compared on governing equations, turbulence and radiation treatment, product and moisture representation, numerical verification and validation error. The review is not systematic and does not report a PRISMA flow diagram or pooled estimates.ResultsOf the 41 sources cited, 14 are primary studies. Only one study applied CFD with a tuber product explicitly represented: a sweet-potato dryer in which the loaded racks were modelled as porous thermal media without moisture transport. In that study, CFD and a lumped-capacitance model both predicted a steady chamber temperature rise of about 40 °C, and fitting the rack heat-transfer coefficients reduced the root-mean-square deviation between the two models to 1.8 °C (lower rack) and 0.9 °C (upper rack); this was a model-to-model comparison, not experimental validation. No CFD study of cassava or yam drying was identified. The only validated cassava heat-and-mass-transfer model was a non-CFD, natural-convection model, with moisture-content differences below 0.2 kg/kg. Guidance on turbulence closure, porous-bed resistance, vapour transport, mesh verification and optimisation was therefore transferred from paddy, fruit, herb and empty-chamber studies, where reported validation errors ranged from 4.3 °C in chamber temperature to below 7.33% in CFD outputs.ConclusionCFD for tuber drying remains largely an inferred rather than demonstrated capability. A decision-driven multiscale framework is proposed that couples loaded-chamber CFD two ways with tuber-specific heat and moisture models, gated by verification, validation and uncertainty analysis; it has not yet been demonstrated on a tuber dryer. Research should prioritise moisture-coupled, experimentally validated tuber CFD and openly shared benchmark data.
Agro-processing is increasingly recognised as a strategic sector for promoting inclusive urban economic development, SMME growth, and employment creation within sustainable food systems. This study examines SMME participation and employment outcomes within Johannesburg’s urban food system, drawing on evidence from Gauteng Province and South Africa more broadly. A scoping review design was adopted, using structured searches of Scopus, ScienceDirect, and Google Scholar for literature published between 2020 and 2025. Eligible sources included peer-reviewed articles, government policy developments, and institutional reports addressing agro-processing, value chains, and SMME development. Of the 40 evidence sources included (28 peer-reviewed articles, eight government policy developments, and four institutional/grey literature reports), 12 focused specifically on Johannesburg, 10 on the wider Gauteng City Region, and 18 provided national or international perspectives. Findings indicate that agro-processing has considerable potential for enterprise development and employment creation; however, SMMEs face persistent constraints in finance, food safety compliance, infrastructure, logistics, and market access. Opportunities exist through niche urban markets, supplier development programmes, shared infrastructure, and cooperative business models. The evidence base remains uneven, with a predominance of qualitative case studies and limited firm-level or city-specific analyses, particularly in distribution and retail stages. This review contributes originality by highlighting the potential application of a life-cycle perspective to agro-processing in emerging cities. It concludes that strengthening SMME participation requires coordinated, context-specific interventions across finance, compliance, infrastructure, procurement, and research to ensure equitable inclusion in Johannesburg’s urban food system.
The preservation of volatile flavour compounds in foods remains challenging because of evaporation, oxidation, thermal and photo-induced degradation, and partitioning within complex food matrices. Microencapsulation provides established protection and scalable processing routes, whereas nanoencapsulation can offer additional control over interfacial area, dispersion, carrier structure, and release. This review critically compares micro- and nano-encapsulation for flavour preservation, distinguishing retention during manufacture from stability during storage and flavour release and perception during consumption. It examines nanoemulsions, nanoliposomes, solid lipid nanoparticles, nanostructured lipid carriers, protein- and polysaccharide-based nanocarriers, and selected hybrid systems, with emphasis on flavour chemistry, carrier–flavour interactions, release mechanisms, food-matrix effects, quantitative performance metrics, and sensory consequences. Nano-enabled packaging and nanosensing are considered only as indirect preservation or monitoring technologies and are explicitly distinguished from direct flavour delivery. The review further evaluates industrial scale-up, cost, technology readiness, safety, migration, regulatory requirements, and sustainability, alongside emerging approaches including AI-assisted formulation, machine learning, digital-twin modelling, microfluidic manufacturing, and precision flavour delivery. Overall, the review identifies when nanoencapsulation provides a meaningful technological advantage over microencapsulation and when conventional microencapsulation may remain the more practical choice.
The brewing industry, long celebrated for transforming beer from barley into one of the world’s most recognizable beverages, is now entering a new era driven by scientific precision and process improvement. Modern breweries are turning to advanced fermentation, enzyme manipulation, and integrated bioprocessing strategies to elevate ethanol production while maintaining the craftsmanship and quality attributes central to brewing heritage. This systematic review, structured according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, compiles emerging industrial advancements that enhance ethanol production during brewing. Development in barley-based brewing focuses on starch structure and composition, varietal improvement through breeding, and pre-treatment approaches to accelerate the release of fermentable sugars. Process refinements in malting, mashing, adjunct use as substrates, and metal ion supplementation are critically analyzed for their impact on sugar release and fermentation performance. In parallel, innovative fermentation platforms, including batch, continuous, fed-batch, high-brewing, and co-culture systems that integrate non-conventional yeasts or bacteria with conventional yeasts, are being explored for their potential to enhance ethanol yield, fermentation duration, and product quality. This review also highlights automation, enzyme-driven hydrolysis optimization, advanced fermentation strategies, and machine-learning-assisted process control as transformative tools shaping the next-generation of brewery-integrated ethanol production. Collectively, these synergistic developments position barley at the forefront of advanced brewing-centered ethanol improvement, reinforcing the brewery as both a cultural tradition and a modern bioprocessing system.
IntroductionAutomated plant leaf disease segmentation is crucial for accurate disease diagnosis and quantitative lesion assessment in precision agriculture. U-Net-based architectures are widely employed for this task, although their high computational complexity sometimes hinders their application in resource-constrained agricultural settings.MethodsThis work investigates depthwise separable convolutions in conjunction with a channel-reversed U-Net for tomato leaf disease segmentation. The channel-reversed encoder allocates more feature channels to the shallow layers and progressively fewer to the deeper layers, the reverse of a conventional U-Net’s channel schedule, with the aim of preserving spatial detail while reducing computation in the deeper, more computationally expensive stages. Depthwise separable convolutions are used throughout the encoder, bottleneck, and decoder to further reduce trainable parameters. The model was trained on lesion masks and evaluated on an independent, held-out subset of the PlantVillage tomato leaf dataset using Dice coefficient, IoU, precision, recall, and F1-score, following a corrected data-splitting protocol that eliminates train/test leakage present in an earlier version of this work.ResultsThe proposed method reduced trainable parameters by over 97% relative to a conventional U-Net (0.96 million versus 31.06 million), at a measured reduction in segmentation accuracy under the training budget evaluated (Dice coefficient 0.79 versus 0.90 for standard U-Net). A supplementary experiment with a longer training budget narrowed this gap substantially (Dice coefficient 0.85).DiscussionThese findings indicate that the parameter-efficient design requires more training to approach the accuracy of higher-parameter alternatives. We report this accuracy/efficiency trade-off directly, rather than claiming accuracy parity, and identify a full matched-budget comparison across all evaluated architectures as a priority for follow-up work. By demonstrating a substantial, disclosed parameter-efficiency gain, this approach is intended to support future development of low-cost disease diagnosis tools for resource-constrained agricultural settings. Validated real-time, on-device deployment remains a direction for future work.
