Search Results (381)

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

This article critically considers sample multigenerational oral traditions of Numic-speaking communities known as the Nüümü (Northern Paiute), Nuwu (Southern Paiute), and Newe (Western Shoshone), written down over the last 151 years. Utilizing the GOAT! phenomenological method to compare the onto-epistemologies of Numic peoples with a wide range of data from (G)eology, (O)ral traditions, (A)rchaeology and (A)nthropology, and (T)raditional knowledge, the author analyzed 824 multigenerational ancestral teachings. These descriptions encode multigenerational memories of potential geological, climatic, and ecological observations and interpretations of multiple locations and earth processes throughout the Numic Aboriginal homelands within California and the Great Basin. Through this layered and comparative analysis, the author identified potential convergences of oral traditions, ethnography, ethnohistory, rock art, and geological processes in the regions of California, the Great Basin, and the Colorado Plateau, indicative of large-scale earth changes, cognized by Numic Indigenous communities as earth birthing events, occurring during the Late Pleistocene/Early Holocene to Middle and Late Holocene, including the Late Dry Period, Medieval Climatic Anomaly, and Little Ice Age. Full article

An innovative approach is explored to reconnect urban populations with nature through the integration of technology and artistic expression. In a case study of London’s Canary Wharf, environmental sensor data of sound and visual art were analyzed to create new pathways for human–plant interaction. By transforming plant biological data into accessible artistic experiences, interdisciplinary methods spanning environmental science, plant biology, and artistic practice can enhance ecological awareness and engagement. The synthesized approach in this study offers promising solutions for addressing the growing disconnect between urban communities and their natural environment. Full article

As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure. Full article

This paper introduces a novel bio-inspired metaheuristic algorithm, named the sharpbelly fish optimizer (SFO), inspired by the collective ecological behaviors of the sharpbelly fish. The algorithm integrates four biologically motivated strategies—(1) fitness-driven fast swimming, (2) convergence-guided gathering, (3) stagnation-triggered dispersal, and (4) disturbance-induced escape—which synergistically enhance the balance between global exploration and local exploitation. To assess its performance, the proposed SFO is evaluated on the CEC2022 benchmark suite under various dimensions. The experimental results demonstrate that SFO consistently achieves competitive or superior optimization accuracy and convergence speed compared to seven state-of-the-art metaheuristic algorithms. Furthermore, the algorithm is applied to three classical constrained engineering design problems: pressure vessel, speed reducer, and gear train design. In these applications, SFO exhibits strong robustness and solution quality, validating its potential as a general-purpose optimization tool for complex real-world problems. These findings highlight SFO’s effectiveness in tackling nonlinear, constrained, and multimodal optimization tasks, with promising applicability in diverse engineering scenarios. Full article

The presence of persistent contaminants in soils is of growing concern around the world. Contaminated soils can affect numerous ecological environments and lead to significant health risks to humans, affecting soil biodiversity, structure and geomechanical behaviour and agricultural sustainability. Additionally, soil contaminants can also leach into water flows, which is another concern. In general, soil contamination can be attributed to natural sources or to anthropogenic sources associated with human activity. Soil contaminants are usually classified in the following categories: biological, radioactive, organic and inorganic contaminants. State of the art information regarding some of the most common persistent soil contaminants, including possible sources and prevalence, and monitoring approaches and information about their effects on soil characteristics, including usability, as well as information on possible mobility to other environmental media is presented in this review paper. Finally, a comprehensive overview of remediation strategies which are being developed, including the more traditional ones as well as novel strategies that have been proposed lately by the scientific community, is provided. This includes physicochemical and biological technologies, as well as mixed remediation technologies aimed at enhancing remediation efficiency. Full article

The longstanding debate over early hominin subsistence strategies, particularly the hunting-versus-scavenging hypothesis, as well as discussions regarding the functionality of Oldowan sites, has been primarily centered on the archeological and paleoanthropological record of Olduvai Gorge. Historically, FLK Zinj has been at the core of these debates, serving as a principal empirical reference due to the prevailing assumption that most other Bed I sites at Olduvai represented non-anthropogenic accumulations However, recent discoveries have significantly reshaped this perspective. Newly identified early sites, including PTK, DS, and AGS, situated within the paleolandscape and thin stratigraphic context of FLK Zinj, provide crucial new anthropogenic datasets. These sites offer additional dimensions to the study of early hominin behavior, facilitating a more nuanced reconstruction of their adaptive strategies in this paleoenvironment. Furthermore, methodological advancements in recent years—including controlled experimental and actualistic studies, sophisticated statistical modeling, and the integration of machine learning algorithms—have greatly enhanced the analytical frameworks available for investigating early hominin behavior. These innovations have refined the ability to formulate and test hypotheses within a rigorous scientific paradigm, significantly improving the resolution of archeological and taphonomic interpretations. This study presents an in-depth taphonomic analysis of the faunal assemblage from level 22A at DS, a Bed I site at Olduvai Gorge dated to approximately 1.84 Ma. The assemblage exhibits exceptional preservation, enabling detailed assessments of skeletal part representation, fragmentation patterns, and surface modifications. By combining traditional taphonomic methodologies with state-of-the-art AI-driven bone surface modification (BSM) analyses, this research contributes novel insights into the interactions between early hominins and carnivores, elucidating the complex ecological dynamics of an Early Pleistocene African paleolandscape. Full article

Kumbapattu is a folk song of the indigenous Kurichiya community sung during Thira, a religious festival celebrated during the month of Kumbham (February). It narrates the mythical life and actions of Malakkari, an embodiment of Lord Shiva and the chief deity of the Kurichiya. A critical study of this 1051-line folk song, its ritual performance, and its ecological fountainheads can contribute to our understanding of the cultural and ritualistic energies and functions of indigenous art forms. This paper examines the role played by religious folk songs in reiterating Kurichiya identity and community integration, and the relevance of such narratives in addressing ecological challenges while sustaining cultural heritage. The method of close textual analysis of Kumbapattu is employed to decode the religious concepts and philosophies of the community, supplemented by observations of ritual performances during fieldwork. This study draws on both primary and secondary materials for the analysis. The study employs Bronisław Malinowski’s myth–ritual theory to examine the relationship between myth and ritual and their role in shaping the Kurichiya identity. Further, William R. Bascom’s four functional categories are applied to identify the ecological functions expressed through the song, since the community is traditionally agrarian and still largely depends on forest and environment for a significant part of their community life. To provide a culturally grounded interpretation that reflects Kurichiya worldviews, the study also incorporates indigenous epistemology to make the analysis more relevant and comprehensive. Full article

Artificial-intelligence-based robotics has recently garnered considerable attention, with the prediction of sample attributes becoming essential for artificial-intelligence-based environmental data analysis and decision-making processes in smart equipment and IoT devices. Based on a masked autoencoder (MAE), this study introduces the FedMAE regressor, a federated learning regression framework designed to precisely predict critical nutrients such as nitrogen, phosphorus, and potassium in agricultural and environmental monitoring devices while ensuring data privacy. The proposed adaptive regressor integrates deep learning methodologies within a federated learning architecture. Layer normalization is employed to enhance the model’s stability in distributed environments, and its structure is optimized with residual connections and GELU activation functions. An adaptive normalization method, a multi-layer feature transformation system, and a balanced data allocation technique are introduced to mitigate data distribution biases in edge devices. Furthermore, the AdaBelief optimizer and a dynamic learning rate scheduling approach are implemented to improve the model’s resilience. Experimental results show that the proposed method outperforms baseline and state-of-the-art models in terms of nitrogen prediction and demonstrates notable adaptability in phosphorus and potassium prediction tasks. This research paves the way for the application of federated-learning-based approaches in various ecological and industrial contexts, providing a robust solution for time-series prediction challenges in diverse domains. Full article

This study analyses the state-of-the-art application of Life Cycle Assessment (LCA) in the production and recycling of rare earth elements (REEs), highlighting its strategic role in promoting sustainability across resource-intensive sectors. A systematic literature review (SLR) was conducted in accordance with PRISMA guidelines using the Scopus database. A total of 78 peer-reviewed studies were included, with no time restrictions applied. The review focused on studies applying LCA to REE production from both primary and secondary sources, particularly those integrating emerging digital technologies such as artificial intelligence, big data, and process simulations. Studies lacking LCA methodology or not specifically addressing REEs were excluded. The findings show that LCA, when enhanced by digital tools, serves as a key enabler for making industrial processes more sustainable by improving traceability, reducing environmental impacts, and supporting responsible decision making along the value chain. Recycling from secondary sources such as electronic waste emerges as a practical solution to reduce dependency on primary resources and to promote circular models. In particular, recycling has been shown to reduce environmental impacts by 64–96%, underscoring its effectiveness in mitigating the ecological footprint of REE production. The innovative contribution of this study lies in demonstrating how the integration of LCA and digital technologies can accelerate the transition toward more sustainable, resilient, and transparent rare earth value chains. Full article

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

The research explores three interconnected themes: philosophy, education, and ecology. It aims to be an interdisciplinary study that emphasizes the significance of the philosophy of environmental education and its practical implications. Initially, it addresses the contemporary hylomorphic production approach, followed by proposing educational solutions aimed at fostering a comprehensive understanding of the environment. This understanding includes recognizing humans as part of the environment, sharing equal rights to existence with all other life forms. The study advocates for a shift away from anthropocentrism, positioning humans in a non-privileged role within the ecosystem. It seeks to challenge long-standing notions where humans have historically placed themselves above other beings. The research is particularly inspired by the “Green Schools” in Bali, which embody a proactive educational philosophy aimed at reshaping how future generations perceive their role in production and environmental stewardship. These schools promote an educational framework that encourages students to reconnect with nature and develop sustainable practices from the ground up, moving away from exploitative and profit-driven paradigms. An example of this innovative approach is found in disciplines such as “eco-art,” where colors are derived from natural relationships rather than manufactured. The overarching goal is to cultivate a perspective that sees humans as integral components of nature, valuing it for its intrinsic worth rather than solely for its utility to humanity. Full article

Suaeda salsa, a halophytic plant species, exhibits a remarkable salt tolerance and demonstrates a significant phytoremediation potential through its capacity to absorb and accumulate saline ions and heavy metals from soil substrates, thereby contributing to soil quality amelioration. Furthermore, this species serves as a critical habitat component for avifauna populations and represents a keystone species in maintaining ecological stability within estuarine and coastal wetland ecosystems. With the development and maturity of UAV remote sensing technology in recent years, the advantages of using UAV imagery to extract weak targets are becoming more and more obvious. In this paper, for Suaeda salsa, which is a weak target with a sparse distribution and inconspicuous features, relying on the high-resolution and spatial information-rich features of UAV imagery, we establish an adaptive contextual information extraction deep learning semantic segment model (ACI-Unet), which can solve the problem of recognizing Suaeda salsa from high-precision UAV imagery. The precise extraction of Suaeda salsa was completed in the coastal wetland area of Dongying City, Shandong Province, China. This paper achieves the following research results: (1) An Adaptive Context Information Extraction module based on large kernel convolution and an attention mechanism is designed; this module functions as a multi-scale feature extractor without altering the spatial resolution, enabling a seamless integration into diverse network architectures to enhance the context-aware feature representation. (2) The proposed ACI-Unet (Adaptive Context Information U-Net) model achieves a high-precision identification of Suaeda salsa in UAV imagery, demonstrating a robust performance across heterogeneous morphologies, densities, and scales of Suaeda salsa populations. Evaluation metrics including the accuracy, recall, F1 score, and mIou all exceed 90%. (3) Comparative experiments with state-of-the-art semantic segmentation models reveal that our framework significantly improves the extraction accuracy, particularly for low-contrast and diminutive Suaeda salsa targets. The model accurately delineates fine-grained spatial distribution patterns of Suaeda salsa, outperforming existing approaches in capturing ecologically critical structural details. Full article