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Potato plant disease detection: leveraging hybrid deep learning models
Agriculture, a crucial sector for global economic development and sustainable food production, faces significant challenges in detecting and managing crop diseases. These diseases can greatly impact yield and productivity, making early and accurate detection vital, especially in staple crops like potatoes. Traditional manual methods, as well as some existing machine learning and deep learning techniques, often lack accuracy and generalizability due to factors such as variability in real-world conditions. This study proposes a novel approach to improve potato plant disease detection and identification using a hybrid deep-learning model, EfficientNetV2B3+ViT. This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). It has been trained on a diverse potato leaf image dataset, the “Potato Leaf Disease Dataset”, which reflects real-world agricultural conditions. The proposed model achieved an accuracy of 85.06, representing an 11.43 improvement over the results of the previous study. These results highlight the effectiveness of the hybrid model in complex agricultural settings and its potential to improve potato plant disease detection and identification.
BioMed Central (BMC)
2025
2025
The complex and dynamic nature of airborne fine particulate matter (PM2.5) has hindered understanding of its chemical composition, sources, and toxic effects. In the first steps of a larger study, here, we aimed to elucidate relationships between source regions, ambient conditions, and the chemical composition in water extracts of PM2.5 samples (n = 85) collected over 16 months at an observatory in the Yellow Sea. In each extract, we quantified elements and major ions and profiled the complex mixtures of organic compounds by nontarget mass spectrometry. More than 50,000 nontarget features were detected, and by consensus of in silico tools, we assigned a molecular formula to 13,907 features. Oxygenated compounds were most prominent, followed by mixed nitrogenated/oxygenated compounds, organic sulfates, and sulfonates. Spectral matching enabled identification or structural annotation of 43 substances, and a workflow involving SIRIUS and MS-DIAL software enabled annotation of 74 unknown per- and polyfluoroalkyl substances with primary source regions in China and the Korean Peninsula. Multivariate modeling revealed seasonal variations in chemistry, attributable to the combination of warmer temperatures and maritime source regions in summer and to cooler temperatures and source regions of China in winter.
2025
More than 70 years of industrial production of per- and polyfluoroalkyl substances (PFAS) have resulted in their ubiquitous presence in the environment on a global scale, although differences in sources, transport and fate lead to variability of occurrence in the environment. Gull eggs are excellent bioindicators of environmental pollution, especially for persistent organic pollutants such as PFAS, known to bioaccumulate in organisms and to be deposited in bird eggs by maternal transfer. Using yellow-legged gull (Larus michahellis) eggs, we investigated the occurrence of more than 30 PFAS, including the most common chemicals (i.e., legacy PFAS) as well as their alternatives (i.e., emerging PFAS) in the Bay of Marseille, the second largest city in France. Compared to eggs from other colonies along the Mediterranean coast, those from Marseille had PFAS concentrations ranging from slightly higher to up to four times lower, suggesting that this area cannot be specifically identified as a hotspot for these compounds. We also found several emerging PFAS including 8:2 and 10:2 FTS, 7:3 FTCA or PFECHS in all collected eggs. Although the scarcity in toxicity thresholds for seabirds, especially during embryogenesis, does not enable any precise statement about the risks faced by this population, this study contributes to the effort in documenting legacy PFAS contamination on Mediterranean coasts while providing valuable novel inputs on PFAS of emerging concern. Identifying exposure in free-ranging species also participate to determine the main target for toxicity testing in wildlife.
Elsevier
2025
This document provides technical details and support for the implementation of air quality monitoring under the Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on ambient air quality and cleaner air for Europe (recast) (AAQD, Directive (EU) 2024/2881). It presents an overview of current knowledge and best practices, signposting to existing technical guidance on air quality monitoring and to sources of ongoing technical guidance development. This document does not formulate any legal provisions and as such, it does not have a legally binding value.
Publications Office of the European Union/European Commission. Directorate-General for Environment
2025
Carbonaceous aerosols (CA), composed of black carbon (BC) and organic matter (OM), significantly impact the climate. Light absorption properties of CA, particularly of BC and brown carbon (BrC), are crucial due to their contribution to global and regional warming. We present the absorption properties of BC (bAbs,BC) and BrC (bAbs,BrC) inferred using Aethalometer data from 44 European sites covering different environments (traffic (TR), urban (UB), suburban (SUB), regional background (RB) and mountain (M)). Absorption coefficients showed a clear relationship with station setting decreasing as follows: TR > UB > SUB > RB > M, with exceptions. The contribution of bAbs,BrC to total absorption (bAbs), i.e. %AbsBrC, was lower at traffic sites (11–20 %), exceeding 30 % at some SUB and RB sites. Low AAE values were observed at TR sites, due to the dominance of internal combustion emissions, and at some remote RB/M sites, likely due to the lack of proximity to BrC sources, insufficient secondary processes generating BrC or the effect of photobleaching during transport. Higher bAbs and AAE were observed in Central/Eastern Europe compared to Western/Northern Europe, due to higher coal and biomass burning emissions in the east. Seasonal analysis showed increased bAbs, bAbs,BC, bAbs,BrC in winter, with stronger %AbsBrC, leading to higher AAE. Diel cycles of bAbs,BC peaked during morning and evening rush hours, whereas bAbs,BrC, %AbsBrC, AAE, and AAEBrC peaked at night when emissions from household activities accumulated. Decade-long trends analyses demonstrated a decrease in bAbs, due to reduction of BC emissions, while bAbs,BrC and AAE increased, suggesting a shift in CA composition, with a relative increase in BrC over BC. This study provides a unique dataset to assess the BrC effects on climate and confirms that BrC can contribute significantly to UV–VIS radiation presenting highly variable absorption properties in Europe.
Elsevier
2025
2025
This report examines the impact of air pollution from residential wood combustion on health in the Nordic countries.Residential wood combustion is a major contributor to premature deaths and health issues. The number of premature deaths is expected to decrease from 1,600 in 2019 to 1,200 by 2030, with health costs dropping from EUR 3.2 bn. to EUR 2.5 bn. This improvement is due to fewer and newer, less polluting appliances, and better energy efficiency in homes.
Two additional scenarios for 2030 reflecting national differences were evaluated.
Technology Scenario: Faster replacement of old appliances, reducing premature deaths by 190 and health costs by EUR 390 mil.
Zone-Based Scenario: Bans in densely populated areas, reducing premature deaths by 240 and health costs by EUR 510 mil.
Mitigation in densely populated areas offers greater health benefits than national-level efforts.
Nordic Council of Ministers
2025
2025
2025
CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials
The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced in vitro models, in silico approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems. Significant progress has been made in generating atomistic and quantum-mechanical descriptors for various NMs, evaluating their interactions with biological systems (from small molecules or metabolites, to proteins, cells, organisms, animals, humans and ecosystems), and in developing predictive models for NMs risk assessment. The CompSafeNano project has also focused on implementing and further standardising data reporting templates and enhancing data management practices, ensuring adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Despite challenges, such as limited regulatory acceptance of New Approach Methodologies (NAMs) currently, which has implications for predictive nanosafety assessment, CompSafeNano has successfully developed tools and models that are integral to the safety evaluation of NMs, and that enable the extensive datasets on NMs safety to be utilised for the re-design of NMs that are inherently safer, including through prediction of the acquired biomolecule coronas which provide the biological or environmental identities to NMs, promoting their sustainable use in diverse applications. Future efforts will concentrate on further refining these models, expanding the NanoPharos Database, and working with regulatory stakeholders thereby fostering the widespread adoption of SbD practices across the nanotechnology sector. CompSafeNano's integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, position the project as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety.
Elsevier
2025
2025
We assessed the biomass burning (BB) smoke aerosol optical depth (AOD) simulations of 11 global models that participated in the AeroCom phase III BB emission experiment. By comparing multi-model simulations and satellite observations in the vicinity of fires over 13 regions globally, we (1) assess model-simulated BB AOD performance as an indication of smoke source–strength, (2) identify regions where the common emission dataset used by the models might underestimate or overestimate smoke sources, and (3) assess model diversity and identify underlying causes as much as possible. Using satellite-derived AOD snapshots to constrain source strength works best where BB smoke from active sources dominates background non-BB aerosol, such as in boreal forest regions and over South America and southern hemispheric Africa. The comparison is inconclusive where the total AOD is low, as in many agricultural burning areas, and where the background is high, such as parts of India and China. Many inter-model BB AOD differences can be traced to differences in values for the mass ratio of organic aerosol to organic carbon, the BB aerosol mass extinction efficiency, and the aerosol loss rate from each model. The results point to a need for increased numbers of available BB cases for study in some regions and especially to a need for more extensive regional-to-global-scale measurements of aerosol loss rates and of detailed particle microphysical and optical properties; this would both better constrain models and help distinguish BB from other aerosol types in satellite retrievals. More generally, there is the need for additional efforts at constraining aerosol source strength and other model attributes with multi-platform observations.
2025
Sb-PiPLU: A Novel Parametric Activation Function for Deep Learning
The choice of activation function—particularly non-linear ones—plays a vital role in enhancing the classification performance of deep neural networks. In recent years, a variety of non-linear activation functions have been proposed. However, many of these suffer from drawbacks that limit the effectiveness of deep learning models. Common issues include the dying neuron problem, bias shift, gradient explosion, and vanishing gradients. To address these challenges, we introduce a new activation function: Softsign-based Piecewise Parametric Linear Unit (Sb-PiPLU). This function offers improved non-linear approximation capabilities for neural networks. Its piecewise, parametric design allows for greater adaptability and flexibility, which in turn enhances overall model performance. We evaluated Sb-PiPLU through a series of image classification experiments across various Convolutional Neural Network (CNN) architectures. Additionally, we assessed its memory usage and computational cost, demonstrating that Sb-PiPLU is both stable and efficient in practical applications. Our experimental results show that Sb-PiPLU consistently outperforms conventional activation functions in both classification accuracy and computational efficiency. It achieved higher accuracy on multiple benchmark datasets, including CIFAR-10, CINIC-10, MWD, Brain Tumor, and SVHN, surpassing widely-used functions such as ReLU and Tanh. Due to its flexibility and robustness, Sb-PiPLU is particularly well-suited for complex image classification tasks.
IEEE (Institute of Electrical and Electronics Engineers)
2025
American Geophysical Union (AGU)
2025