With the worsening environment quality credited to rapid urbanization and industrialization, ensuring optimal indoor air quality is a critical challenge with far-reaching implications for public health, environmental sustainability, and overall wellbeing. Despite its importance, real-time air quality monitoring solutions (AQMS) remain underdeveloped, leaving significant gaps in the ability to analyze and predict environment quality effectively. In this paper, we have presented the development of a Machine Learning Internet of Things (ML-IoT) enabled AQMS equipped with diverse sensors capable of sensing vital environmental parameters which include carbon monoxide, carbon dioxide, particulate matter PM2.5, PM10, temperature, and humidity. We deployed the developed AQMS in an indoor environment and collected environmental data from sensors. A comprehensive preprocessing pipeline, including outlier removal using the interquartile range method and feature scaling, was applied to improve data quality. Three machine learning models including Linear Regression (LR), Random Forest (RF), and Bidirectional Long Short-Term Memory (BiLSTM) were implemented for predictive analysis. Using state-of-the-art data analytics, we have tested these ML models to uncover trends, identify correlations, and predict air quality metrics with improved accuracy. To ensure methodological rigor, 5-fold cross-validation was applied to LR and RF models, while time-series cross-validation was used for BiLSTM to preserve temporal dependencies. The results show that LR achieves high accuracy for temperature prediction with
R
2
of 96.45% and cross validation
R
2
of 95.4%, while RF provides moderate performance for CO
2
prediction with
R
2
of 52.7% and 64.05% in cross validation. BiLSTM improves CO
2
prediction with
R
2
of 89.9%) under standard evaluation and achieves an average
R
2
of 85.4% under time-series validation, demonstrating generalization. To provide real-time visualization of air quality parameters, we have designed a user-friendly dashboard, allowing stakeholders to monitor real-time conditions and derive actionable insights. The developed AQMS finds promising application in industries, houses, office and residential buildings, allowing predictive environment quality monitoring and triggering alarm in case of any anomaly.
Tanzila, Sundus Ali, M. Aslam et al.· Frontiers in Environmental S...· 0 citations
Introduction Timely and accurate detection of plant diseases is essential for ensuring global food security and supporting sustainable agriculture. Conventional diagnostic approaches, such as manual inspection and laboratory testing, are often time-consuming, labor-intensive, and impractical for large-scale or remote agricultural environments. Although deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved automated plant disease classification, they often lack interpretability and struggle to generalize under diverse field conditions. Methods This study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance. The SE blocks recalibrate channel-wise feature responses to emphasize disease-relevant information while suppressing background noise. To improve model transparency, Grad-CAM, SHAP, and LIME were incorporated to provide visual and feature-level explanations of the model’s predictions. The framework was trained and evaluated using the Plant Pathology 2020 dataset containing four classes: healthy, rust, scab, and multiple diseases. Results AgriX-SENet achieved a training accuracy of 97.47% and a validation accuracy of 95.07%, outperforming fourteen state-of-the-art deep learning models. The classification report demonstrated high precision and recall across most disease categories, although the scab class exhibited comparatively lower recall, indicating an opportunity for further improvement. The explainability analyses consistently showed that the model focused on pathologically relevant regions of leaf images, validating the reliability of its predictions. Discussion The proposed AgriX-SENet framework effectively combines high classification performance with model interpretability, addressing a key limitation of existing CNN-based plant disease detection systems. Its ability to provide accurate and explainable predictions makes it a promising solution for scalable agricultural diagnostics. Future work will focus on improving classification performance for challenging disease categories and optimizing the framework for deployment on mobile and edge computing devices to enable real-time field applications.
S. Raj, Prashant Johri, Vishwadeepak Singh Baghela et al.· Frontiers in Plant Science· 1 citation
Nonterrestrial networks (NTNs) based on low-Earth-orbit (LEO) satellite constellations provide promising platforms for conducting global-scale federated learning (FL). However, a fundamental feasibility barrier remains: transmitting full-model updates often exceeds the typical LEO visibility windows (30–90 s), resulting in systematic client dropouts and unstable training processes. The existing approaches largely treat communication efficiency and satellite topologies independently, leaving this challenge unresolved. In this paper, we propose a unified NTN-aware FL framework that integrates low-rank adaptation (LoRA) with a three-tier hierarchical aggregation architecture. We show that LoRA resolves the feasibility barrier not only through incremental compression but also by reducing the uplink payload size by <inline-formula> <tex-math notation="LaTeX">$74.6\times $ </tex-math></inline-formula>, thereby shifting the system bottleneck from communication-limited operations to computation-limited operations. This shift enables a hierarchical aggregation scheme that is otherwise infeasible under LEO visibility constraints. The proposed three-tier protocol combines satellite-side weighted aggregation, intersatellite link (ISL)-averaging consensus, and gateway-level global aggregation. To address intermittent connectivity issues, we further develop a staleness-aware asynchronous extension with a satellite-tailored discount function. In addition, personalized LoRA adapters enable client-specific adaptations to be implemented under heterogeneous channel conditions. We establish a rigorous NTN system model that captures topology dynamics and visibility constraints and prove its convergence under standard nonconvex assumptions. Simulations performed under realistic NTN settings demonstrate that the proposed method achieves <inline-formula> <tex-math notation="LaTeX">${R} ^{2}$ </tex-math></inline-formula> = 91.3%, closely matching full-model FL (91.8%) with a <inline-formula> <tex-math notation="LaTeX">$74.6\times $ </tex-math></inline-formula> parameter reduction. In terms of latency, the uplink contribution is reduced from 55.8% to 4.0%, while client-side computations account for 93.8% of the end-to-end latency. In terms of reliability, the client dropout rate is lowered from 65.3% to 25.4%. Regarding efficiency, ISL traffic is reduced by <inline-formula> <tex-math notation="LaTeX">$23\times $ </tex-math></inline-formula> while achieving sublinear round completion scaling up to 10,000 clients.
Muhammad Shoaib Ayub, A. Khan, Felipe Augusto Pereira et al.· IEEE Open Journal of the Com...· 0 citations