Search Results (1,577)

The transition from conventional silica nanoparticle (SiNP) production based on purified alkoxysilanes and high-temperature flame hydrolysis of silicon tetrachloride to renewable and waste-derived silicon resources requires more than precursor substitution. It requires a mechanistic understanding of how feedstock mineralogy, silicon speciation, impurity chemistry, and processing history propagate through dissolution, nucleation, condensation, gelation, aging, drying, and pore evolution to determine material performance, environmental burden, and manufacturing feasibility. Although previous reviews have established the technical feasibility of producing silica from secondary resources, their predominant organization by feedstock, synthesis route, or application provides limited ability to explain why nominally similar processes generate materials with markedly different structural and functional properties. This review addresses these through a resource-pull, feedstock-to-function framework that links resource chemistry and process design to critical material attributes, application-specific specifications, sustainability, and scale-up requirements. Agricultural residues, industrial by-products, geothermal resources, waste glass, and fluorosilicate streams are critically compared according to silicon form and phase, reactivity, impurity profile, compositional variability, purification demand, and attainable product quality. Particular attention is given to waste-derived alkaline silicate systems, in which molecular, oligomeric, and colloidal silica coexist and therefore require characterization beyond bulk SiO₂ concentration. Established and emerging processing strategies, including controlled combustion and alkaline extraction, alkali fusion, ambient-pressure drying, microwave and mechanochemical activation, biogenic and biomimetic templating, and continuous processing, are evaluated according to their mechanistic effects, technological maturity, structural control, and demands for energy, reagents, water, solvents, effluent treatment, and capital. Across these routes, gelation and aging emerge as critical transfer stages through which feedstock composition is translated into network connectivity, pore architecture, shrinkage behavior, and ultimately functional performance. Evidence from secondary-source aerogels further shows that properly controlled waste-derived systems can attain BET surface areas of approximately 350–500 m² g⁻¹, within the textural range of many alkoxide-derived materials, indicating that feedstock variability, impurity management, and process control are more important constraints than an inherently lower performance ceiling. On this basis, this review proposes a minimum evidence framework comprising feedstock traceability, intermediate-speciation and colloidal characterization, silicon mass balance, gelation and aging metrics, application-specific qualification criteria, performance-normalized life cycle and techno-economic assessment, process analytical control, and staged pilot validation. Collectively, these principles provide a mechanistically grounded basis for moving sustainable silica synthesis beyond isolated proof-of-concept demonstrations toward reproducible, scalable, application-matched, and commercially credible manufacturing platforms. Full article

Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems. Full article

Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter wheat mapping in the Chaohu Basin, China, using a reconstructed 5-day Sentinel-2 NDVI time series and precipitation data from the Global Precipitation Measurement (GPM) mission. The model employs a dual-branch architecture to jointly learn vegetation and precipitation features and introduces a lag-aware dynamic gated fusion module to capture the delayed response of vegetation to precipitation and enhance multi-source feature fusion. The results show that the proposed method achieved reliable early-season winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional methods, including SVM, RF, DTW, and TCN, using the same reconstructed 5-day NDVI time series. Optimal performance uses a 7 × 7 patch size and 30-day precipitation window. Full article

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural systems. It first clarifies the conceptual boundaries of agricultural intelligent agents and distinguishes them from traditional multi-agent systems, agent-based modeling, agricultural foundation models, and static retrieval-augmented question-answering systems. It then synthesizes their architectural foundations, key capabilities, application scenarios, deployment challenges, and future research directions. The reviewed literature indicates that agricultural intelligent agents are moving beyond isolated perception, prediction, and response generation toward the goal-oriented coordination of agricultural knowledge, dynamic data, external tools, and decision-making processes across agricultural task chains. They are beginning to support more integrated forms of knowledge services, crop monitoring and diagnosis, decision support, and farm-level collaborative management. Nevertheless, their transition from prototype systems to dependable and deployable agricultural systems remains constrained by context-aware knowledge grounding, heterogeneous data and tool integration, long-horizon reliability, the stability of multi-agent collaboration, and system security. This review further introduces an assessment perspective based on evidence reported in the original studies, comparing representative agricultural intelligent agents in terms of task decomposition, agronomic evidence applicability, tool-use validity, workflow reliability, multi-agent coordination, and deployment-related evidence. By distinguishing demonstrated capabilities from unevaluated dimensions, this review provides a structured framework for understanding the current status of agricultural intelligent agents and for guiding their future development toward reliable, deployable, and domain-oriented intelligent systems for smart agriculture. Full article

Image-level monitoring and variety-level detection of three specialty tomato cultivars, Kiss, Millennium, and White Jade, remain challenging in unstructured orchards because of foliage occlusion, overlapping fruit clusters, and variable illumination. Conventional downsampling may weaken fine spatial details of small targets, whereas larger detectors can impose computational demands that are unsuitable for mobile or edge-based agricultural platforms. To address these limitations, we propose WCAF-YOLO, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture. The model replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information. Its weighted channel-aware fusion (WCAF) neck combines learnable branch weighting with parameter-free three-dimensional attention to refine fused features. Bounding-box regression uses focaler-minimum point distance intersection over union (Focaler-MPDIoU). Across five random seed runs on the internal held-out test subset of a custom single-site orchard dataset, WCAF-YOLO obtained a mean ${\mathrm{mAP}}_{50–95}$ of $0.9048\pm 0.0013$ and a mean recall of $0.9280\pm 0.0019$. The corresponding mean improvements over the YOLOv26n baseline were 2.14 and 3.42 percentage points, respectively. The model contained 2.36 M parameters and required 6.36 GFLOPs. Under the evaluated protocol, the model combined a compact parameter count with higher mean detection metrics than the YOLOv26n baseline. The detector outputs two-dimensional bounding boxes and variety labels for image-level orchard monitoring and variety-level assessment. Integration into agricultural field platforms remains to be validated. Full article

To elucidate how roots respond to hydroponics, we investigated the mechanisms by which nutrient solution flow influences root growth in hydroponic lettuce via phenotypic analysis combined with single-cell RNA sequencing. Nutrient solution flow exerted a dual-phase effect on lettuce root growth, characterized by initial inhibition followed by subsequent promotion. Although initially, root biomass and morphological indices were significantly lower under flow treatment than under static treatment, this trend rapidly reversed by days 2 and 3 and all the measured indices showed improved root growth under flow treatment. Single-cell transcriptomic analysis enabled the construction of a comprehensive cellular atlas of hydroponic lettuce roots, which indicated heterogeneous transcriptional responses for lettuce roots under static and flow treatments. Flow treatment altered root cell composition, inducing decreases in initial cells and increases in vascular cells. Pseudotime trajectory analysis suggested that the differentiation of initial cells into vascular tissues was associated with plant hormone signaling and MAPK pathway-related gene expression, and also revealed differential expression of key functional genes, including ACO3 in root cap cells and CAM7 in xylem cells. Therefore, this study provides insights into the transcriptional regulatory framework of hydroponic lettuce roots in response to nutrient solution flow, which may provide a basis for optimizing hydroponic crop production via rhizosphere environment regulation. Full article

Despite the rapid proliferation of unmanned aerial vehicles (UAVs) across industrial, agricultural, and scientific domains, their deployment remains constrained by limited endurance, aerodynamic inefficiency, and acoustic emissions, all mediated by a shared onboard energy budget. Existing biomimetic UAV reviews have generally treated aerodynamics, structures, sensing, control, and energy systems as parallel topics rather than as interacting components of a unified aerial architecture. Drawing primarily on literature published between 2015 and June 2026 and identified through searches of Web of Science, Scopus, and Google Scholar, this review addresses this gap by examining bio-inspired technologies across six principal domains: aeroacoustic and passive flow control, aerodynamic efficiency, multifunctional structural composites, neuromorphic sensing and control, ionic energy storage, and energy harvesting. Its principal contribution is a cross-domain synergy analysis identifying five performance couplings and one structural enabling architecture through which these domains interact physically and functionally. Representative examples include serration-based propeller geometries that can simultaneously reduce noise and power demand; morphing wing surfaces that serve as both aerodynamic structures and triboelectric harvesting substrates; and neuromorphic spiking neural networks that have been reported, in specific event-vision inference benchmarks, to reduce inference energy by three to four orders of magnitude relative to embedded graphics processing unit (GPU)-based implementations. Mechanical harvesting outputs nonetheless remain orders of magnitude below propulsion requirements and are thus positioned as supplementary. Four systemic barriers (unquantified mass–energy balance, undocumented durability, aeroelastic co-design gaps, and heterogeneous metrics) are evaluated, and the resulting synthesis indicates that advancing bio-inspired UAVs requires a transition from structural imitation to functional, system-level biomimetics. Full article

Accurate localization of bounding boxes is a prerequisite for enabling vision-driven precision agriculture pipelines, as downstream tasks such as morphological feature extraction, growth monitoring, and digital-twin synchronization depend directly on the geometric quality of the detected boxes. Distance-IoU (DIoU) and Complete-IoU (CIoU) improve upon simple overlap-based objectives by incorporating a normalized center-distance term into the regression loss, along with the overlap and, for CIoU, an aspect-ratio penalty, but that term remains embedded in a single composite formulation with an implicit, non-adjustable weight. We propose a Directional Alignment Penalty (DAP), an auxiliary localization regularizer that introduces an independently weighted normalized center-displacement term into the bounding-box regression objective without modifying the detector architecture. The proposed DAP-YOLOv8 increased mAP@0.5:0.95 from 51.69% to 53.76% and mAP@0.5 from 80.03% to 80.36% while preserving precision and recall; repeated-seed experiments further showed that the improvement in fine-grained localization was consistent across random initializations on a purpose-built oyster-mushroom (Pleurotus ostreatus) dataset collected from a real-world smart greenhouse. Results show that explicitly modeling the center-distance factor as an independent and tunable component can improve fine-grained localization without sacrificing the computational efficiency of the base detector, thereby providing a lightweight plug-in extension for agricultural detection and digital-twin applications. Full article

Vernacular heritage conservation often separates material fabric, environmental adaptation, and social meaning. This study addresses that separation for one settlement. Socio-environmental heritage is defined here as heritage whose significance resides in the documented interdependence of environmental conditions, material practice, social organisation, and customary governance, rather than in fabric or imagery alone. The single purpose of the article is to develop and demonstrate an evidence-based method for interpreting and conserving Thee Ain Heritage Village in Al-Baha, Saudi Arabia, as such a system. A qualitative architectural case-study design combines a structured literature search, regional comparison, the author’s 2014 field observations and photographs, and published digital-heritage, energy-retrofit, and conservation studies; no human-participant data are analysed. Evidence is organised through three analytical layers—climatic material, socio-spatial, and customary governance—and each evidence–interpretation proposition is classified as directly observed, supported architectural inference, or hypothesis requiring measurement; conservation translation is treated as the output of this sequence rather than as a parallel analytical layer. Coded claim units are documented individually so that every interpretation and implication can be traced to its source, strength, and limitation. The analysis links rocky siting, stone and timber assemblies, thick load-bearing madameek walls, limited openings, vertical domestic hierarchy, controlled thresholds, and the agricultural setting to conservation priorities at landscape, construction, spatial, and adaptation scales. These priorities include compatible repair, retention of wall depth and opening logic, protection of privacy gradients and threshold sequences, and service integration without reducing the village to stone-clad imagery. Unlike previous work centred on digital documentation, energy modelling, or policy-level preservation, the contribution is an evidence-structured method linking architectural observation to bounded interpretation, conservation decisions, and explicit future testing requirements. Full article

Biochar is a porous carbonaceous material synthesized through the pyrolysis of diverse biomass resources, including agricultural and forestry residues as well as livestock manure. It possesses superior characteristics such as a large specific surface area, adjustable pore architecture, abundant surface functional groups, and favorable electrical conductivity. With the increasingly severe global energy shortage and environmental pollution problems in recent years, biochar has emerged as a green, low-cost functional material with distinct application superiority in multiple key research directions, including energy storage and conversion, chemical catalysis, environmental restoration, and signal sensing and detection. This study comprehensively summarizes the latest research advances of biochar in the aforementioned application fields, focusing on innovative achievements in photocatalytic and electrocatalytic hydrogen generation, supercapacitors and electrochemical energy storage systems, persulfate activation technology, carbon dioxide capture, remediation of heavy metal and organic contaminants, volatile organic compound (VOC) adsorption, as well as electrochemical sensing devices. Existing research results demonstrate that modification strategies including metal and non-metal doping, surface oxidation treatment, and compounding with semiconductors or metal oxide materials can effectively improve the catalytic activity and functional performance of biochar. Furthermore, this paper prospects the future interdisciplinary development trends of biochar, analyzes the existing research gaps in mechanism exploration, structural optimization design, and industrial large-scale preparation, and provides theoretical and practical references for the further popularization and application of biochar in sustainable energy development and environmental governance fields. Full article

Automatic classification of Ataulfo mango (Mangifera indica L.) ripeness is essential to ensure consistent quality, standardize post-harvest processes, and reduce the subjectivity of traditional visual inspection, which is unreliable and error-prone. This paper aims to develop a computationally efficient image classification system for Ataulfo mango ripeness by combining explicit color and texture feature extraction with a traditional machine learning model, thereby reducing the high computational costs typically associated with deep convolutional architectures. For this purpose, a dataset containing 10,400 images was created and divided into four maturity categories: green-ripe, partially ripe, firm-ripe, and soft-ripe. We select an optimal Multilayer Perceptron trained on compact 33-dimensional feature vectors and compare its performance with classical machine learning algorithms and pretrained deep neural networks, including MobileNetV2, MobileNetV3, and ResNet18. Our proposal achieves an accuracy of 0.8821, a macro-F1 score of 0.8784, and an AUC of 0.9751, which are better than those of classical classifiers and MobileNet-family models, while reducing computational cost by three orders of magnitude (GFLOPs). The ResNet18 model achieved a 3.56% relative improvement in macro-F1 score compared to our proposal, but its computational cost increased by four orders of magnitude in GFLOPS. In all evaluated architectures, the remaining classification errors occur between adjacent maturity stages and likely reflect the visual similarity inherent in the continuous ripening process. These findings demonstrate that manual feature engineering and model selection via hyperparameter tuning remain highly competitive and more sustainable for low-cost edge implementations in agriculture. Full article

The objective of this research was to develop a system capable of detecting diseases in maize leaves through the analysis of foliar images, using machine learning techniques to support early diagnosis in the agricultural sector. A custom dataset of field-captured images was built, and a preprocessing workflow was applied that included data augmentation, segmentation, feature extraction, and normalization. Subsequently, three models: EfficientNetB0, DenseNet121, and EKNN were trained and evaluated to determine the architecture with the best classification performance. The results showed that model performance varied according to how each approach processed visual features, with the EKNN model achieving the highest overall accuracy of 94.33%, outperforming EfficientNetB0 (89.70%) and DenseNet121 (88.04%). The CNN-based architectures achieved adequate classification in diseases with well-defined patterns but presented limitations when dealing with visually similar lesions. In contrast, the EKNN model, which relies on segmentation and enhanced feature extraction, achieved the best overall performance, demonstrating the importance of preprocessing in diagnostic accuracy. Finally, the selected model was integrated into a functional web application, validating its practical utility as a tool for the early detection of diseases in maize leaves. This research demonstrates that machine learning can effectively assist farmers and agricultural technicians in the efficient identification of plant diseases, contributing to improved productivity and better decision-making in the field. Full article

Plant pests and diseases pose a major global threat to food security and agricultural sustainability, making accurate assessment of plant symptoms important. This study proposes a simple and practical method for quantifying visual symptom severity using binary deep learning classifiers trained only on asymptomatic and severely affected images. Visual symptom severity was estimated from class probabilities, and classification accuracy is additionally used when image groups with similar symptom severity are available. While the framework enables symptom severity quantification at the group level, direct application to individual images is challenging due to overconfident predictions for mildly symptomatic cases. To address this issue, temperature scaling and label smoothing were evaluated, and label smoothing was found to improve reliability for individual image assessment. The method was validated using mosaic and wilting symptoms across multiple architectures. Results showed close agreement in classification accuracy and class probabilities across architectures, with maximum differences of 4.8% (mosaic) and 9.7% (wilting). The estimated visual symptom severity was generally consistent with expert visual assessment under the conditions examined in this study. The proposed method requires only minimal annotation and uses standard model outputs, making it simple, interpretable, and potentially applicable to other visually assessed traits. Full article

Sustainable food system transformation is inseparable from planetary health, the interdependent wellbeing of human populations and the ecological systems that sustain them. Agroecology and regenerative agriculture are frequently portrayed as competing paradigms for sustainable food system transformation. This commentary challenges this framing as both analytically unwarranted and practically counterproductive. Grounded in Ethiopia’s smallholder agricultural context and informed by recent empirical evidence, we argue that these two approaches are complementary and mutually reinforcing. Agroecology emerged from an equity-centered, participatory, and politically engaged framework for transforming food systems, while regenerative agriculture contributes farm-level ecological restoration tools and measurable biophysical outcomes related to planetary health. Neither is sufficient alone: regenerative practices without agroecology’s social architecture risk reinforcing existing inequities; agroecology without a systematic effort to ensure a widespread increase in production for food security and a net positive regeneration of environmental health cannot fully deliver its transformative potential, particularly in contexts where productivity, livelihoods, and ecosystem restoration must be achieved Ethiopia’s concurrent validation of a National Agroecology Strategy (2026–2040) and advancement of a National Regenerative Agriculture Strategy (2026–2036) reflects a growing national recognition that these paradigms are essential partners, not rivals. An integrated framework embedding regenerative practices within agroecological principles offers the most scientifically grounded and contextually appropriate pathway for Ethiopia’s resilient, equitable, and productive agricultural transformation. Reframed in these terms, agroecology–regenerative integration is not simply a technical or sectoral fix but a direct contribution to planetary health, safeguarding the ecological systems, food security, and social equity on which human wellbeing ultimately depends. Full article

Renewable-powered agricultural water systems are often assessed as solar-pumping devices, but their sustainability depends on a service chain linking crop-water demand, hydraulic duty point, power electronics, storage, water quality, governance, operation and end-of-life management. This structured integrative review synthesizes peer-reviewed and practice-oriented evidence on photovoltaic pumping, hybrid renewable irrigation, grid-interactive pumps, micro-hydro assistance and renewable-powered brackish-water reverse osmosis (PV-RO). Evidence was screened across four source families and coded by service function, energy architecture, hydraulic duty and dominant sustainability pathway; recurring combinations were consolidated using explicit separation and merge rules. It develops an archetype-based screening framework for ex ante appraisal of irrigation, desalination and circularity risks. Seven technology-service archetypes are identified: direct PV pumping, PV-to-tank pumping, PV with electrical buffering, grid-interactive PV pumping, PV–wind hybrid irrigation, micro-hydro-assisted irrigation and PV-RO water making. The framework links each archetype to its operating envelope, evidence maturity, enabling subsystems, sustainability pathways, minimum indicators and ordinal triggers for deeper due diligence. Hydraulic storage is usually the lowest-regret reliability buffer for open-field irrigation, whereas batteries are justified mainly when pressure stability, fertigation timing or night-time operation has high agronomic value. PV-RO is a distinct water-making archetype and is environmentally defensible only where feed-water characterization, energy recovery, pretreatment, product-water agronomy, membrane management and permitted concentrate disposal are embedded in design. Two synthetic applications demonstrate archetype selection and due-diligence escalation. Responsible deployment requires service-oriented screening that integrates hydraulic design, groundwater governance, procurement quality assurance, circularity obligations and social inclusion before field implementation. Full article