Open Access

Recent advances in deep neural networks in terms of convolutional neural networks (CNNs) have enabled researchers to significantly improve the accuracy and speed of object recognition systems and their application to plant disease and pest detection and diagnosis. This paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios. The review shows that only a few studies available in the literature used data from real agricultural fields such as the PlantDoc dataset. The paper also reveals overoptimistic results of the huge number of studies in the literature that used the PlantVillage dataset collected under (controlled) laboratory conditions. This finding is consistent with the characteristics of the dataset, which consists of leaf images with a uniform background. The uniformity of the background images facilitates object detection and classification, resulting in higher performance-metric values for the models. However, such models are not very useful in agricultural practice, and it remains desirable to establish large datasets of plant diseases under real conditions. With some of the self-generated datasets from real agricultural fields reviewed in this paper, high performance values above 90% can be achieved by applying different (improved) CNN architectures such as Faster R-CNN and YOLO.

Rupture discs, also known as bursting discs, are indispensable components in fluid-operated systems providing effective protection against hazardous over-pressure or partial vacuum. They belong to a special class of safety devices and are found in a variety of technical applications including pressure vessels, piping systems, reactors and boilers. In all application scenarios, rupture discs act as sacrificial parts that have to fail precisely at a predetermined differential pressure, opening a relief flow path for the working fluid. The membrane employed within rupture discs is usually made out of specific metal alloys or different material layers depending on the particular application. However, for many manufacturers of rupture discs, the production process is characterized by a lack of systematic procedures, relying instead on trial and error as well as empirical values. By means of thorough finite-element-based modeling and simulation of the bulge-forming process of rupture discs, including an elastic–plastic material law, large deformation, as well as contact mechanics, it is possible to accurately predict the resulting stress–strain behavior. All simulation results are rigorously validated through corresponding experiments conducted during the bulge-forming process. Therefore, this contribution provides a reliable basis for the parameter set-up during the manufacturing process of rupture discs.

This study investigated the perception of drought by cocoa farmers and explored the effectiveness of adaptive strategies (ASs) used in smallholding farms in the transboundary region between Ghana and Togo. Drought significantly threatens cocoa production in this region, affecting farmers’ livelihoods and cocoa supply chains. This study used a multistage sampling approach, which involved surveys with questionnaires administered to 330 cocoa farmers throughout the study area, along with on-site observations. Statistical analysis included binary logistic and Poisson regression models to explore the relationship between farmer socioeconomic characteristics and adaptation practices. The findings revealed that cocoa farmers in the region have a nuanced understanding of drought, attributed to changing climatic patterns and unsustainable land management practices such as deforestation. To mitigate its impacts, farmers employ a variety of ASs, including investment in farm management, soil management, and intercropping with crop diversification. Furthermore, socioeconomic factors, including age, formal education, household size, land tenure right, adaptation cost assessment, and an underestimation of self-efficacy, were shown to affect the choice in the AS. Among the ASs adopted, only farm management practices (weeding, pruning, fertilizer application, etc.) significantly improved the cocoa yield. This study contributes to understanding drought as a critical issue for cocoa farmers and the adaptation practices used by smallholder cocoa farmers. Given that among the strategies adopted, only farm management practices, also known as good agricultural practices (GAPs), significantly improves yield, this study recommends well-designed and innovative packages of sustainable farm management based on farm and owner characteristics. These include irrigation schemes, timely soil fertilizer monitoring and supply, and the provision of drought-resistant varieties along with technical itineraries. Additional interventions require drought emergency responses, with other factors such as education and financial support mechanisms expected to improve farmers’ timely decision-making to adapt and improve cocoa production resilience to drought episodes in international transboundary regions with complex governance structures.

Transformative disaster resilience in times of climate change underscores the importance of reflexive governance, facilitation of socio-technical advancement, co-creation of knowledge, and innovative and bottom-up approaches. However, implementing these capacity-building processes by relying on census-based datasets and nomothetic (or top-down) approaches remains challenging for many jurisdictions. Web 2.0 knowledge sharing via online social networks, whereas, provides a unique opportunity and valuable data sources to complement existing approaches, understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, this study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people’s experiences and knowledge regarding flooding disasters and highlighted communities’ sociocultural characteristics. This bottom-up approach could be an innovative alternative to traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives.

In this contribution, the effectiveness of helical static mixers in different arrangements and flow configurations/regimes is explored. By means of a thorough numerical analysis, the application limits of helical static mixers for the heat transfer enhancement inside cooling channels of machine tools are provided. The numerical simulations were processed with the commercial finite volume Computational Fluid Dynamics (CFD) code, ANSYS Fluent 2020 R2. This study shows that there exists an optimal range of application for static mixers as heat exchange intensifier depending on the flow speed, the transmitted heat flow and the thermal conductivity of the tool. The investigations of this contribution are restricted to single-phase flow in circular cross-sections and straight channel geometries. As a representative application example for a machine tooling, the cooling of a simple injection mold is investigated. The research carried out reveals that the application of static mixing elements for enhancement of heat transfer is very effective, particularly for fluid flow with low to medium Reynolds numbers, close-contour cooling, high values of heat fluxes, and high thermal conductivity of the tooling material.