Search Results (334)

To address the environmental hazards caused by massive bauxite red mud stockpiles, phase-change unburned bricks have been developed. The slump flow and initial setting time of fresh mortar were tested, while the flexural strength, compressive strength, splitting tensile strength and rebound hardness of hardened mortar specimens were measured. The synergistic influence of stearic acid on mechanical strengths, rebound hardness and thermal conductivity was revealed, and the corresponding indoor simulation tests were performed. X-ray diffraction (XRD), scanning electron microscopy (SEM) and Ultra-depth-of-field microscope cross-section scanning were adopted to interpret the intrinsic microstructure and inner mechanism. Results indicate that slump flow, initial setting time and all mechanical indices follow cubic functional relationships with red mud mass ratio. Specimens incorporating 20 wt.% red mud achieve the optimal mechanical strength, rebound hardness and thermal conductivity, with the maximum growth rates of up to 32.7%, 18.7% and 27.0%, respectively. Appropriately, straw fibers improve the mechanical properties and rebound hardness yet reduce thermal conductivity. In simulated thermal cabin tests, wall temperature continuously rises under heating and declines after heat termination; red mud and straw fibers jointly slow the heating-up rate and post-heating cooling rate. Samples with 5 wt.% red mud possess the densest hydration matrix. Red mud promotes the generation of ettringite (AFt), calcium carbonate and dolomite crystals, and elevates the content of dicalcium silicate (C₂S) within the binder system. This study provides a reference for fabricating functional wall materials using industrial solid waste (red mud) and agricultural solid waste (straw fibers). Full article

To address the evaluation requirements for agricultural robotic harvesting, this work presented a fruit-grasping assessment system based on tactile fusion analysis. Four piezoresistive pressure sensors were symmetrically integrated into the inner surfaces of a flexible gripper. A signal-conditioning circuit and a data acquisition module transmitted tactile signals to a Transformer–Mamba fusion network for feature extraction and target classification. After being trained on a dataset comprising 300 samples, the model extracted deep tactile features to distinguish among three target categories: citrus fruits, branches, and leaves. Classification outputs generated control commands for a robotic manipulator, enabling obstacle-avoidance retraction and precise harvesting operations. Experimental evaluations, conducted in both indoor and outdoor environments, demonstrated a target recognition accuracy of 93.12%. The manipulator response time was below 0.5 s, and the operational success rate was 90%. The proposed sensing system and algorithmic framework showed strong adaptability and supported quantitative assessment of grasping performance. The fruit detachment, compression damage, and plant-collision risks were effectively reduced while operational stability and harvesting efficiency improved. Full article

In Mediterranean countries such as Portugal, summer heatwaves increasingly threaten agricultural productivity by disrupting crop physiological processes. Greenhouse cultivation often exacerbates heat stress, while conventional cooling systems such as air conditioning and evaporative cooling impose unsustainable energy demands. This study investigates an Earth-to-Air Heat Exchanger (EAHE) system consisting of a five-tier helical PVC pipe configuration (29 m, buried at a depth of 3 m), installed in a prototype polycarbonate greenhouse in Covilhã, Portugal, and monitored under real summer conditions. Four ventilation scenarios were simulated in EnergyPlus 25.1, and a segmented NTU thermal model, implemented as a Python plugin via the pyenergyplus API, predicted the EAHE outlet temperature with CV(RMSE) values of 1.47% at 30 m³/h and 3.0% at 50 m³/h. The IPMA meteorological dataset provided the best simulation accuracy (RMSE = 2.31 °C, R² = 0.978). In simulations based on the experimentally calibrated models, EAHE preconditioning reduced accumulated heat stress degree-hours above 28 °C by 9.1 to 9.5% and lowered peak indoor temperature by up to 2.60 °C, at system COPs of 6.9 to 10.6, which are 2.3 to 3.5 times higher than conventional vapour-compression cooling; propagated measurement uncertainties confirm the robustness of this COP advantage. A model-based parametric scale analysis indicated that geometrically scaled circuits (DN200, DN400) achieve degree-hour reductions of 67 and 91%, supporting EAHE scalability through geometric proportioning, pending experimental validation at larger scales. Full article

Unmanned Aerial Vehicles (UAVs) are seeing increased use in agricultural settings due to their potential to be integrated with systems for applying pesticides. They can target specific areas while offering the potential to reduce chemical waste and improve application efficiency. However, this means that spray deposition efficiency is strongly influenced by rotor-induced downwash, which affects droplet transport, drift, and uniformity. This study presents a combined computational and experimental investigation of downwash–spray interactions in a hexacopter platform. CFD is used to predict the performance of various sprayer configurations that differ in the number, spacing, and positioning of nozzles. Rotor-induced airflow is modeled using an actuator disk approach in ANSYS Fluent 2025, and spray behavior is predicted using the Discrete Phase Model. Pure water was used as the working fluid for both the CFD simulations and experimental validation to ensure consistency between numerical and physical testing conditions. Numerical results indicate that a two-nozzle under-rotor setup maximizes performance characteristics such as deposition area, density, and uniformity for the designed agricultural UAV, providing a theoretically effective deposition area of 9.375 m², an effective application rate of 0.03387 mL/m², and a coefficient of variation of 45.3%. Compared to the best-performing boom configuration, this represents an approximately 13.5% improvement in spray uniformity. These results are validated through experimental testing using a modular UAV sprayer system and deposition measurements obtained from water-sensitive paper in controlled indoor conditions, achieving a droplet size of 502 µm, swath width of 1.8 m, 0.8% area coverage, and a coefficient of variation of 36.5%. While differences were observed between predicted and measured droplet size distributions, the CFD and experimental results demonstrated similar trends in spray coverage and deposition uniformity. Future work will refine simulations to better match experimental conditions and investigate canopy interaction, crosswind effects, and field-scale performance. Full article

Grazing systems, commonly considered less productive than indoor livestock farming, account for most farms and agricultural land worldwide. Beyond feeding livestock, they fulfil essential environmental, economic, and social functions that are particularly relevant to sustainable development in the context of climate change, resource constraints, and population growth. By relying on circular resource use and promoting ecological processes, grazing systems can offset methane emissions from livestock through soil carbon sequestration while preserving biodiversity, maintaining landscapes, and reducing fire risks. In order to assess whether grazing systems are actually less productive than indoor systems, data from comparative trials assessing these two feeding environments published over 40 years were used as the basis for a meta-analysis published in 2014. This analysis showed that animal performance on pasture was equivalent to that indoors, despite the supplementation provided primarily to indoor feeding environments. Given the economic and environmental context and the challenges related to the sustainable development of livestock farming, here, we present this 2014 data to encourage its equitable reuse and contribute to a better overall assessment of grazing in current debates, considering all aspects, including animal performance. Furthermore, supplementing this initial dataset with additional data will lead to an even more comprehensive evaluation of these grass-based livestock systems. Full article

The automation of fruit harvesting has become an important research topic in precision agriculture due to increasing labor shortages, rising production costs, and the need for improved harvesting efficiency. Among horticultural crops, strawberries present particular challenges for robotic harvesting because of their variability in size, shape, ripeness, and frequent occlusions caused by leaves and surrounding fruit. The objective of this work is to demonstrate the feasibility of a reproducible perception-to-manipulation framework for robotic strawberry harvesting based on commercially available hardware and established computer vision techniques, rather than to propose a novel object detection algorithm. The proposed system integrates a YOLOv3-based (You Only Look Once) object detector, monocular vision for fruit localization, and a Universal Robots UR5e collaborative manipulator. Strawberry coordinates estimated from monocular images are transformed into the robot reference frame and transmitted through the XML-RPC (Extensible Markup Language-Remote Procedure Call) protocol, enabling robot positioning. The system was experimentally validated in a controlled indoor environment under different artificial illumination conditions. The YOLOv3 detector achieved a ${\mathrm{mAP}}_{0.5:0.95}$ of 37.4%, a precision of 84.2%, a recall of 76.1%, and a latency of 6.5 ms per image (153.8 FPS). The experiments also demonstrated reliable communication between the perception and robotic manipulation modules, enabling the robotic arm to reach the estimated strawberry positions. The proposed framework provides a practical and low-cost solution for integrating deep-learning-based perception with robotic manipulation and establishes a solid basis for future work on localization accuracy, automated grasping, harvesting efficiency, and deployment in real agricultural environments. Full article

The year-round rising demand for fresh, high-quality vegetables has driven rapid growth in South Korea’s protected horticulture since the 1990s, resulting in widespread greenhouse installations across South Korea. However, maintaining optimal indoor environmental conditions in greenhouses remains challenging owing to extreme seasonal variations. During summer, indoor temperatures may exceed 35 °C despite active cooling systems; meanwhile, large temperature gradients between the indoor and outdoor environments require effective heating strategies in the winter. A key technology for stabilizing crop productivity and mitigating spatial environmental imbalances is the use of air circulation fans, which promote uniform distribution of temperature, humidity, and CO₂. This study investigates the airflow dispersion characteristics of agricultural circulation fans using computational fluid dynamics (CFD) simulations to support improved airflow distribution within greenhouses. The target facility was a multi-span Venlo-type greenhouse. Six circulation fans were installed 5.8 m above the ground, and their airflow patterns were analyzed under different layout scenarios, including uniform spacing and zigzag arrangements. The results showed that a single fan generated an effective airflow area of up to 193.14 m² and a dispersion distance of 60.34 m. When all fans were aligned in the same direction, airflow distribution was less efficient compared with configurations where central fans were reversed or installed in a zigzag pattern. Specifically, staggered arrangements improved the overall airflow distribution, with the volume-averaged air velocity increasing from 0.290 to 0.369 m/s. The study concludes that fan installation spacing and arrangement significantly influence airflow distribution and uniformity in greenhouses. Full article

To address the challenges of large morphological variations of weed targets, background interference in close-range agricultural images, and limited computational resources for visual perception models, this paper proposes a sequential visual perception method for weed recognition and short-range 3D localization in controlled or semi-controlled close-range precision weeding scenarios. The proposed method consists of two main stages: weed detection and 3D localization. In the detection stage, a lightweight MCS-YOLOv8 model is constructed based on YOLOv8n. MobileNetV3 is introduced to reduce the number of parameters and computational complexity, while CBAM and Shape-IoU are adopted to enhance weed-related feature representation and improve bounding-box regression for irregular weed targets. In the localization stage, RAFT-Stereo is used as the initial disparity estimator, and a detection-guided adaptive WLS depth optimization strategy is designed by using the detection bounding boxes and confidence scores. This strategy refines the target-region depth information and supports short-range 3D coordinate estimation. Experimental results show that MCS-YOLOv8 contains 1.6 M parameters and requires 4.3 GFLOPs. Its mAP@0.5 and mAP@0.5:0.95 reached 94.1% and 65.0%, respectively, which were 2.0 and 2.7 percentage points higher than those of the YOLOv8n baseline. Meanwhile, the number of parameters and FLOPs were reduced by approximately 46.7% and 46.9%, respectively. In the 3D localization experiments under controlled conditions, the mean absolute errors in the X, Y, and Z directions were 2.2 mm, 2.6 mm, and 3.2 mm, respectively, with an average 3D Euclidean error of approximately 4.7 mm. Dynamic target experiments further demonstrated that the proposed pipeline could complete indoor dynamic target recognition, 3D coordinate updating, and laser pointing verification under controlled conditions. The results indicate that the proposed method shows effective weed detection and short-range 3D localization performance under controlled agricultural close-range imaging conditions, and can provide a reference for visual perception and end-effector pointing in controlled or semi-controlled close-range precision weeding equipment. Full article

Greenhouse cultivation in winter faces significant challenges in maintaining suitable air temperature and humidity conditions for crop growth during nighttime. This study proposes an innovative thermal management system that integrates a solar air collector circulation system with clay bricks to regulate the microclimate of plastic greenhouses. Comparative experiments were conducted in Suzhou, China (subtropical monsoon climate), using two identical greenhouses (2.6 m × 1.5 m × 2.0 m) over nine consecutive days in winter. Three experimental scenarios were designed and implemented, and the results demonstrated that the clay brick system improved the greenhouse temperature and humidity regulation performance. Under the relatively optimal schedule (9:00–16:00 external circulation, 16:00–9:00 internal circulation), the average nighttime indoor air temperature was 13.68 °C during the three experimental days. The cumulative suitable temperature duration (10–35 °C) reached 4050 min over the three test days, which was 30.6% higher than that of the ordinary greenhouse, and the suitable relative humidity duration (40–80%) was 1140 min, an increase of 40.7% during the three experimental days. This study innovatively combines low-cost clay bricks with solar air collectors for passive temperature and humidity control in greenhouses and determines the relatively optimal operation schedule for application in winter. Featuring low cost, simple operation and high sustainability, the system provides a novel energy-saving technical solution for microclimate regulation in agricultural greenhouses in winter. Full article

A dual-platform mushroom cultivation system integrating artificial intelligence (AI)-assisted environmental monitoring and controlled-environment agriculture (CEA) was developed to support experiential STEM education across K–12 and undergraduate settings. Hands-on instruction with multicellular fungi is often limited by reliance on microbial models and by constraints associated with field-based activities. To address this gap, we implemented an indoor instructional platform that combines a commercial AI-assisted automated cultivation unit with a tent-based chamber for hands-on environmental control. Representative cultivated species included oyster mushrooms (Pleurotus spp.) and lion’s mane (Hericium erinaceus). The AI-assisted system provided sensor/camera-based monitoring, app-based feedback, and software-assisted regulation of humidity, light, and airflow, whereas the tent-based system enabled direct student manipulation of cultivation conditions. Together, the systems allowed students to observe fungal development, manage environmental parameters, and collect quantitative and qualitative data within a single academic term. Post-harvest activities, including mushroom-based food preparation and tasting, further connected fungal biology with food and sustainability. A matched pre- and post-course survey (n = 30) showed increases in students’ self-reported perceived understanding, cultivation confidence, and engagement, with mean scores increasing from approximately 2–4 to 6–8. Because the survey instrument was not formally validated and no control group was included, these results are interpreted as preliminary self-reported perceptions rather than objective evidence of learning gains. The platform provides a practical model for integrating fungal biology, AI-assisted environmental monitoring, and CEA into STEM education. Full article

Tomato leaf diseases substantially reduce tomato yields and quality and remain a persistent challenge for efficient crop management. Although deep learning-based detectors have achieved strong accuracy in controlled benchmarks, many existing solutions are still difficult to transfer to resource-constrained agricultural systems because they rely on high-end GPUs, consume considerable power, and often lose performance after deployment on embedded devices. To address this practical gap, this study proposes HGS-YOLO, a system-oriented deployable lightweight adaptation of YOLOv11 for leaf-level tomato disease detection, together with an end-to-end edge sensing pipeline for low-power agricultural deployment. The main contribution lies in the coordinated system-level co-design of model structure, optimization, and deployment rather than in a novel detector architecture. Specifically, YOLOv11 is adapted through three coordinated modifications: an HGNetV2 backbone for efficient feature extraction, an HS-FPN neck with channel attention for lightweight multi-scale fusion, and an MPDIoU loss function for more stable localization optimization. Beyond the model architecture, the study establishes a complete engineering pipeline that includes training, optimization, post-training quantization, and hardware deployment with BPU acceleration on a D-Robotics RDK X5 handheld platform. Comprehensive benchmark experiments indicate that HGS-YOLO achieves 93.6% ${\mathrm{mAP}}_{50}$ and 72.1% mAP@[0.5:0.95] with 86.5% recall, only 1.3 M parameters, and a 3.1 MB model size, substantially reducing the model complexity and storage cost relative to the YOLOv11 baseline. A three-seed retraining comparison shows that HGS-YOLO trades roughly 0.5 ${\mathrm{mAP}}_{50}$ points for this compactness (a statistically significant but small concession) and recovers the cost on the deployment side: on the RDK X5 chip, HGS-YOLO is the fastest, most memory-efficient, and lowest-power model among all compared detectors. Indoor deployment tests using separately collected tomato leaf samples further achieve 90.3% ${\mathrm{mAP}}_{50}$, 82.3% recall, 89.0% precision, 25.0 ± 0.4 ms end-to-end latency, 40.0 ± 0.6 FPS, and 9.8 ± 0.4 W average system power. After PTQ, the ${\mathrm{mAP}}_{50}$ drops from 93.6% to 93.0% on the same benchmark; because this figure was measured under controlled imaging conditions, it is presented as an in-distribution reference point rather than as evidence of robustness in the open field. We also took the handheld system into a working tomato greenhouse for a small outdoor field round, where it ran end-to-end and produced on-device disease detections under natural sunlight, specular highlights, partial occlusion, background clutter, and handheld motion blur. These results show that HGS-YOLO reaches a good balance of accuracy, efficiency, and deployability and that it works in the field on an independent small-scale test; validating it more widely across sites, seasons, and weather is left to future work. Full article

Hurricanes cause severe impacts on lives, livelihoods, and essential systems. Hurricane Melissa impacted Jamaica as a Category 5 cyclone, resulting in estimated losses of approximately 41% of national GDP (US$8.8 billion) and eliciting widespread damage to housing, healthcare, agriculture, and urban infrastructure. Agriculture sustained heavy losses, with 41,000 hectares of damaged farmland and the loss of more than 1 million livestock animals. These impacts resulted in exposed animal closures with biological hazards. Using systems thinking, the PESTHEEL framework, and a One Health lens, we argue for viewing Hurricane Melissa as series of cascading inter-related One Health threats of waterborne and vector-borne diseases, zoonoses, antimicrobial resistance, degraded indoor and outdoor air quality, chemical pollution, and shifting migration and border dynamics. These each unfold at different timings. A structured synthesis for Jamaica and other Caribbean Small Island Developing States is provided by integrating systems thinking, One Health, and the PESTHEEL framework. Immediate and lagged risk pathways are identified, and practical risk reduction actions are proposed to support anticipatory, multisectoral recovery: enhanced syndromic, laboratory, wastewater, vector, and rodent surveillance; resilient WASH and shelter systems; non-insecticidal and integrated vector management; biosecure aid and border protocols; environmental toxicology monitoring; and climate–health intelligence. Full article

Saline water irrigation has emerged as a promising approach to mitigate agricultural water shortages; however, its improper use may induce secondary soil salinization. In this study, saline-alkali soil collected from Hami, Xinjiang, was used to conduct a series of indoor one-dimensional vertical soil column experiments. The aim was to systematically investigate the effects of sodium humate and desulfurization gypsum on soil infiltration behavior and the distribution patterns of key cations and anions under different levels of irrigation water salinity. The results showed that sodium humate application markedly improved soil infiltration capacity, while the duration of infiltration decreased with increasing salinity. Under salinity levels of 12 and 16 g/L, the 4 g/kg sodium humate treatment exhibited the most rapid advancement of the wetting front. In contrast, desulfurization gypsum reduced infiltration rates, with the lowest infiltration observed under the 12.5 g/kg treatment at 16 g/L salinity. Under different treatments, the adjusted coefficients of determination (adjusted R²) for the Philip, Kostiakov, and Horton models ranged from 0.8450 to 0.9841, 0.9901 to 0.9989, and 0.9748 to 0.9942, respectively, while the global performance indicator (GPI) ranged from 1.619 × 10⁻³ to 5.103 × 10⁻¹, 4.998 × 10⁻⁹ to 2.166 × 10⁻⁵, and 1.505 × 10⁻⁶ to 2.438 × 10⁻⁴, respectively. These results indicate that the Kostiakov model outperformed the other models in terms of fitting accuracy and overall performance for describing the soil infiltration process. In addition, sodium humate generally increased the sorptivity parameter S in the Philip model and the empirical coefficient K in the Kostiakov model, whereas desulfurization gypsum showed the opposite trend. In terms of salt regulation, sodium humate demonstrated optimal desalination performance at application rates of 6–8 g/kg under low salinity and 4–6 g/kg under high salinity conditions. Conversely, excessive gypsum application tended to exacerbate salt accumulation, although a moderate dosage (5 g/kg) effectively limited the downward migration and accumulation of Na⁺ and Cl⁻. These two ions were identified as the dominant contributors to soil salinization, showing strong positive correlations with soil salt content (SSC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP). In contrast, Ca²⁺, Mg²⁺, and HCO₃⁻ played beneficial roles in alleviating sodicity through ion exchange and buffering mechanisms. Overall, sodium humate enhanced infiltration and facilitated salt leaching in the upper soil layers under saline irrigation conditions. Although desulfurization gypsum reduced infiltration and increased overall salt content, it contributed to mitigating Na⁺ accumulation in deeper soil profiles. These findings highlight the critical importance of selecting appropriate soil amendments and optimizing their application rates to improve saline water use efficiency and promote sustainable management of saline-alkali soils. Full article

Micro(nano)plastics (MNPs) represent a pervasive and escalating threat to global ecosystems and human health. This review provides a critical synthesis of MNPs’ exposure risks across marine, atmospheric, and terrestrial compartments, with a distinct emphasis on identifying cross-media linkages and methodological inconsistencies that limit current risk assessments. Within marine environments, pollution hazard indices reveal significant spatial heterogeneity, yet their utility is constrained by the absence of toxicity weighting and particle characteristic integration. Atmospheric exposure profiles show variable risks, and the MNPs’ concentration in indoor air (up to 15.8 particles/m³) is significantly higher than in outdoor environments, posing a greater inhalation risk to infants and children who spend more time indoors. A marked increase in MNPs’ concentrations within agricultural soils is identified, where the MNP content in mulched soils (average: 570.2 particles/kg) is more than twice that of non-mulched soils (259.6 particles/kg). Critically, studies have now detected MNPs within human tissues, including the blood, intestines, liver, kidneys, tonsils, and brain, highlighting an urgent need to elucidate their multi-organ toxicity mechanisms, with a novel synthesis of gut–brain axis disruption and transgenerational effects. By integrating exposure dynamics with mechanistic toxicity data, this review advances a cross-system framework that identifies priority research directions, namely standardized detection methodologies, combined pollutant toxicity, and cross-system toxicity mechanisms, which are essential for informing mitigation strategies amid this escalating public health crisis. Full article

Agriculture plays a relevant role in the food supply chain but is also a major contributor in terms of emissions. A possible solution to reduce its impact is to replace traditional machinery with innovative systems, such as agricultural rovers. In the proposed research, a case study of an agricultural rover, specifically designed to operate in orchards, is presented. The powertrain features a Li-ion battery pack as the primary energy source and a fuel cell system operating as a range extender unit. Hydrogen is stored on board using a metal hydride tank to enhance compactness. Once the traction and range extender power output control strategies were defined, experimental tests in a closed warehouse were performed. During the tests, the rover was manually controlled using a joystick, since the main focus was to evaluate the powertrain behavior rather than to test the autonomous driving algorithm. During the tests, different maneuvers in narrow spaces were performed. The results showed that the rover successfully accomplished the tasks and the range extender unit can effectively extend the rover autonomy up to +150% compared to the pure battery solution. This result was obtained considering a 15 min test carried out in an indoor environment with a polished concrete floor. Full article