This study presents a real-time, adaptive UAV system designed to enhance ecological surveys by overcoming the trade-off between wide-area coverage and high-resolution data collection. The Modular Detection and Targeting System (MDTS) integrates thermal imaging for broad detection and high-resolution RGB zoom imaging for precise species identification. Field trials demonstrated the system’s ability to detect and record both avian and mammalian species with significantly reduced redundant data and improved survey efficiency. Compared to traditional UAV methods, the MDTS achieved over 300-fold improvements in image resolution and up to a 1000-fold reduction in data volume. The system’s modular design enables rapid adaptation to diverse ecological applications, providing classification-ready data while minimizing post-processing demands. These results highlight the MDTS as a scalable, efficient tool for wildlife monitoring and environmental research, bridging the gap between detection and actionable ecological insights.

Accurate classification of salt marsh vegetation is vital for conservation efforts and environmental monitoring, particularly given the critical role these ecosystems play as carbon sinks. Understanding and quantifying the extent and types of habitats present in Ireland is essential to support national biodiversity goals and climate action plans. Unmanned Aerial Vehicles (UAVs) equipped with optical sensors offer a powerful means of mapping vegetation in these areas. However, many current studies rely on single-sensor approaches, which can constrain the accuracy of classification and limit our understanding of complex habitat dynamics. This study evaluates the integration of Red-Green-Blue (RGB), Multispectral Imaging (MSI), and Hyperspectral Imaging (HSI) to improve species classification compared to using individual sensors. UAV surveys were conducted with RGB, MSI, and HSI sensors, and the collected data were classified using Random Forest (RF), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms. The classification performance was assessed using Overall Accuracy (OA), Kappa Coefficient (k), Producer’s Accuracy (PA), and User’s Accuracy (UA), for both individual sensor datasets and the fused dataset generated via band stacking. The multi-camera approach achieved a 97% classification accuracy, surpassing the highest accuracy obtained by a single sensor (HSI, 92%). This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. The results suggest that multi-sensor UAV systems offer a cost-effective and efficient approach to ecosystem monitoring, biodiversity assessment, and conservation planning.

Hyperspectral image (HSI) classification is vital for environmental monitoring, land cover mapping, and precision agriculture, but its effectiveness is often constrained by the scarcity of labeled samples and high spectral similarity among classes. To address these challenges, we propose CrossCapsViT, a hybrid classification framework that integrates Capsule Networks (CapsNets) and Vision Transformers (ViTs) through a Cross-Attention Fusion (CAF) mechanism and a cross-layer adaptive fusion module, enabling richer and more discriminative spectral–spatial feature learning. To further improve efficiency in data scarce scenarios, we embed an Actor–Critic reinforcement learning based active learning (RAL) strategy that jointly leverages accuracy, uncertainty, and diversity in the reward structure, guiding the selection of the most informative samples while reducing labeling effort. Experiments conducted on four benchmark datasets (KSC, PU, HU2013, and Salinas) and a custom UAV based saltmarsh dataset (Derrymore, collected with a Pika-L sensor) demonstrate that CrossCapsViT with RAL consistently outperforms CapsViT and other baseline models in terms of classification accuracy, robustness, and generalizability. The proposed framework achieves up to 25% improvement in class-level accuracy on challenging vegetation classes, while reducing dependence on large annotated datasets, highlighting its potential for practical deployment in real-world ecological monitoring and remote sensing applications.

This paper presents the first large scale validation of the Enhanced Newtonian Dynamics framework to the Spitzer Photometry and Accurate Rotation Curves (SPARC) dataset. Unlike traditional models that rely on adjustable free parameters or dark matter halos, END derives gravitational attraction solely from the local density distribution and geometric shape of the galaxy. To accommodate the complexity of real-world data, we developed a hybrid optimisation strategy combining gradient descent with an interval search algorithm. This method enables the robust fitting of multi-component models, where galaxies are decomposed into overlapping oblate spheroids to capture intricate density profiles. After applying strict accuracy filters to the SPARC catalog, we demonstrate that END successfully reproduces the rotation curves of 39 disk galaxies across varying morphological types. Our results confirm that a parameter-free, density-driven approach is sufficient to model galactic dynamics without invoking non-baryonic matter.

Hyperspectral image (HSI) classification is central to environmental monitoring, yet real-time deployment of deep learning models on resource-constrained edge platforms remains challenging due to high spectral dimensionality and computational overhead. In this paper, we propose TinyCapsViT, an ultra-lightweight hybrid architecture that integrates convolutional feature extraction, transformer-based attention, and capsule-inspired representation learning for efficient HSI classification under strict TinyML constraints. The model employs a minimal convolutional stem using pointwise and depthwise separable convolutions to capture local spectral-spatial features, followed by a compact tokenization strategy and learnable positional embeddings. A lightweight self-attention module enables global context modeling with reduced computational complexity, while a capsule-inspired refinement block with squash nonlinearity and residual scaling enhances feature discrimination. The proposed architecture contains only 2781 trainable parameters, representing up to a 30× reduction compared with larger architectures such as ResNet and Vision Transformer, and approximately 11× and 9.5× fewer parameters than the CNN and 3D-CNN baselines, respectively. Extensive experiments across benchmark hyperspectral datasets demonstrate that TinyCapsViT achieves up to 99.61% overall accuracy and maintains competitive classification performance despite its substantially reduced model complexity. Although TinyCapsViT does not consistently achieve the highest classification accuracy compared with larger baseline models, it provides a favourable trade-off between classification performance and computational efficiency. These results demonstrate the potential of TinyCapsViT as a practical solution for real-time hyperspectral analysis on resource-constrained edge platforms and UAV-based environmental monitoring applications.

The study of galactic dynamics remains fragmented across three competing paradigms—Dark Matter ($\Lambda CDM$), Modified Newtonian Dynamics (MOND), and Yukawa-type modifications. This work relies on Enhanced Newtonian Dynamics (END), a parameter-free gravitational framework that derives rotational velocities exclusively from observable baryonic mass distributions and geometric configurations. Applying END to 39 high-quality galaxies from the SPARC database via a three-oblate spheroid composite model (core, bulge, and disc), we achieve mean velocity errors below 3% for 71.79% of the sample, with a median ${\chi }^2=1.77$. The fundamental END relation, $M{T}^2=\kappa V$ (where $\kappa \approx 1.4121\times {10}^{11}{\displaystyle \frac{\mathrm{kg}}{{\mathrm{m}}^3}}{\mathrm{s}}^2$), successfully reproduces observed rotation curves without invoking non-baryonic dark matter haloes or modified gravity interpolation functions. Notable exceptions (e.g., IC2574, ${\chi }^2=51.56$) arise from geometric mismatches between the assumed exponential decay profiles and the actual density structures of dwarf irregular systems. Classical Newtonian Dynamics, applied to identical mass distributions, accounts for only 20% of observed velocities, suggesting the “missing mass” problem reflects limitations in how gravitational law relates enclosed mass to orbital motion rather than unseen matter. END’s zero-parameter architecture renders it strictly falsifiable, offering a parsimonious alternative to parameter-heavy competing models while requiring future iterations to incorporate adaptive geometric baselines for morphologically diverse galaxies.