Search Results (337)

Projects integrating Science, Technology, Engineering, and Mathematics (STEM) are essential to interdisciplinary research. This study presents a STEM (Science, Technology, Engineering, and Mathematics) methodology with the primary objective of designing, constructing, and validating a functional cannabinoid extraction device. To inform the device’s drying parameters, the dehydration kinetics of female hemp buds or flowering buds (FHB) were first analyzed using infrared drying at 100 °C for different durations. The plants were cultivated and harvested in accordance with good agricultural practices using Dinamed CBD Autoflowering seeds. The FHB were harvested and prepared by manually separating them from the stems and leaves. Six 5 g samples were prepared, each with a slab geometry of varying surface area and thickness. Two of these samples were ground: one into a fine powder and the other into a coarse powder. Mathematical fits were obtained for each resulting curve using either an exponential decay model or the logarithmic equation $y\left(t\right)=A\left(e^{-kt}\right)+y_0$ calculate the equilibrium moisture (m_E). The Moisture Rate (MR) was calculated, and by modelling with the logarithmic equation, the constant k and the effective diffusivity (Deff) were determined with the analytical solution of Fick’s second law. The Deff values (ranging from 10⁻⁷ to 10⁻⁵) were higher than previously reported. The coarsely ground powder sample yielded the highest k and Deff values and was selected for oil extraction. The device was then designed using Quality Function Deployment (QFD), specifically the House of Quality (HoQ) matrix, to systematically translate user requirements into technical specifications. A 200 g sample of coarsely ground, dehydrated FHB was prepared for ethanol extraction. Chemical results obtained by Liquid Chromatography coupled with Photodiode Array Detection (LC-PDA) revealed the presence of THC, CBN, CBC, and CBG. The extraction device design was validated using previous results showing the presence of CBD and CBDA. The constructed device successfully extracted cannabinoids, including Δ9-THC, CBG, CBC, and CBN, from coarsely ground FHB, validating the integrated STEM approach. This work demonstrates a practical framework for developing accessible agro-technical devices through interdisciplinary collaboration. Full article

Water-saving agricultural irrigation systems depend heavily on the hydraulic stability of pressure-compensating (PC) emitters, whose performance is fundamentally shaped by internal flow-path geometry. This study analyzes six commercial PC emitters (E1–E6) operated under pressures of 0.8–2.0 bar to quantify how key geometric descriptors influence hydraulic parameters critical for efficient water use, including actual discharge (q_act), discharge coefficient (k), pressure exponent (x), emission uniformity (EU), and flow variability. All emitters had discharge deviations within ±7% of nominal values. Longer and more tortuous labyrinths enhanced compensation stability, while emitters with wider cross-sections and shorter paths produced higher throughput but weaker regulation efficiency. Linear mixed-effects modeling showed that effective flow area increased k, whereas normalized path length and tortuosity reduced both k and x. Predictive equations derived from geometric indicators closely matched measured values, with deviations below ±0.05 L/h for k and ±0.05 for x. These results establish a geometry-based hydraulic framework that supports emitter selection and design in water-saving agricultural irrigation, aligning with broader Agricultural Water–Land–Plant System Engineering objectives and contributing to more efficient and sustainable water-resource utilization. Full article

In soils, it is key to not simply determine the behavior of the major elements but also understand the fate of trace and ultra-trace elements that can often have disproportionate effects on these complex systems. Soils, including agricultural soils, constitute a reservoir of nanoparticles and natural colloids of multiple origins. Nonetheless, only limited information is available on the concentrations and fate of nanoparticles in soils, due largely to the difficulty of distinguishing anthropogenically generated particles from the complex soil matrices in which they are found. Bulk measurements are often unable to quantify the key contributions of trace pollutants (i.e., needle in a haystack); however, single particle techniques have recently become available for studying complex agricultural systems, including soils. For example, the characterization of engineered nanoparticles or incidentally generated particulate pollutants within a natural soil or sediment is now possible using techniques such as single particle inductively coupled plasma mass spectrometry (SP-ICP-MS). Nonetheless, in order to exploit the single particle techniques, it is first necessary to representatively sample the soils. The approach presented here has been designed to help better understand the impact of incidental and engineered nanoparticles on agricultural soils. In this study, we examine two approaches for extracting colloidal particles (CP) from soils in order to facilitate their characterization by single particle inductively coupled plasma mass spectrometry using a sector field- (SP-ICP-SF-MS) and time-of-flight- (SP-ICP-ToF-MS) based instruments. A novel sampling methodology consisting of an ultrasound-assisted continuous-flow extraction (USCFE) was developed and compared to a commonly used batch extraction procedure. Metal containing colloidal particles (M–CP) were quantified and characterized following their extraction in ultrapure water and tetrasodium pyrophosphate (TSPP). At least five successive extraction cycles of 18 h each were required to optimally extract Si–CP (ca. 6 × 10¹⁵ kg⁻¹) using the batch extraction approach, whereas similarly high numbers of CP could be extracted by USCFE in about 3 h. The combined use of continuous flow, ultrasound and TSPP improved the sampling of colloidal particles and nanoparticles from an agricultural soil. Due to its higher sensitivity, SP-ICP-SF-MS was used to measure the smallest detectable M–CP in the soil extracts. SP-ICP-ToF-MS was used to determine the multi-elemental composition of the extracted colloidal particles. Full article

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from $\mu =3.9$ (Challenge phase) to $\mu =4.6$ (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education. Full article

This study analyses the integration of Generative Artificial Intelligence (GenAI) in agro-environmental higher education in Ecuador, focusing on its contribution to sustainable digital transformation aligned with Sustainable Development Goals (SDGs) 4 and 9. The research was conducted at the Faculty of Agricultural and Environmental Engineering (FICAYA) of Universidad Técnica del Norte (UTN) using a quantitative, cross-sectional, and analytical design. A validated digital survey grounded in established technology-acceptance frameworks—the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) was administered to 94% of the student population, showing satisfactory internal consistency (Cronbach’s α = 0.87). Data was analysed using descriptive statistics and multivariate techniques, including Principal Component Analysis (PCA) and k-means clustering. The results obtained in Microsoft Forms^® indicate that ChatGPT-5 is the most widely used GenAI tool (54.2%), followed by Gemini (11.9%). Students reported perceived improvements in academic performance (62.5%), conceptual understanding (74.6%), and task efficiency (69.1%). PCA explained 67% of the total variance, identifying three latent dimensions: effectiveness and satisfaction, institutional access and support, and ethical concerns versus operational benefits. Furthermore, k-means clustering (k = 2) segmented users into two distinct profiles Integrators, characterised by frequent use and positive perceptions, and Cautious Users, exhibiting lower usage and greater ethical or technical concerns. Overall, the findings highlight GenAI as a catalyst for sustainable education and underline the need for institutional and ethical frameworks to support its responsible integration in Latin American universities. Full article

Agricultural systems face mounting pressures from climate change, as rising temperatures, elevated CO₂, and shifting precipitation patterns intensify plant disease outbreaks worldwide. Conventional strategies, such as breeding for resistance, pesticides, and even transgenic approaches, are proving too slow or unsustainable to meet these challenges. Synthetic biology offers a transformative paradigm for reprogramming plant immunity through genetic circuits, RNA-based defences, epigenome engineering, engineered microbiomes, and artificial intelligence (AI). We introduce the concept of synthetic immunity, a unifying framework that extends natural defence layers, PAMP-triggered immunity (PTI), and effector-triggered immunity (ETI). While pests and pathogens continue to undermine global crop productivity, synthetic immunity strategies such as CRISPR-based transcriptional activation, synthetic receptors, and RNA circuit-driven defences offer promising new avenues for enhancing plant resilience. We formalize synthetic immunity as an emerging, integrative concept that unites molecular engineering, regulatory rewiring, epigenetic programming, and microbiome modulation, with AI and computational modelling accelerating their design and climate-smart deployment. This review maps the landscape of synthetic immunity, highlights technological synergies, and outlines a translational roadmap from laboratory design to field application. Responsibly advanced, synthetic immunity represents not only a scientific frontier but also a sustainable foundation for climate-resilient agriculture. Full article

As precision agriculture places higher demands on real-time field weed detection and recognition accuracy, this paper proposes a multi-scale information-enhanced weed detection algorithm, MIE-YOLO (Multi-scale Information Enhanced), for precision agriculture. Based on the popular YOLO12 (You Only Look Once 12) model, MIE-YOLO combines edge-aware multi-scale fusion with additive gated blocks and two-stage self-distillation to boost small-object and boundary detection while staying lightweight. First, the MS-EIS (Multi-Scale-Edge Information Select) architecture is designed to effectively aggregate and select edge and texture information at different scales to enhance fine-grained feature representation. Next, the Add-CGLU (Additive-Convolutional Gated Linear Unit) pyramid network is proposed, which enhances the representational power and information transfer efficiency of multi-scale features through additive fusion and gating mechanisms. Finally, the DEC (Detail-Enhanced Convolution) detection head is introduced to enhance detail and refine the localization of small objects and fuzzy boundaries. To further improve the model’s detection accuracy and generalization performance, the DS (Double Self-Knowledge Distillation) strategy is defined to perform double self-knowledge distillation within the entire network. Experimental results on the custom Weed dataset, which contains 9257 images of eight weed categories, show that MIE-YOLO improves the F1 score by 1.9% and the mAP by 2.0%. Furthermore, it reduces computational parameters by 29.9%, FLOPs by 6.9%, and model size by 17.0%, achieving a runtime speed of 66.2 FPS. MIE-YOLO improves weed detection performance while maintaining a certain level of inference efficiency, providing an effective technical path and engineering implementation reference for intelligent field inspection and precise weed control in precision agriculture. The source code is available on GitHub. Full article

Prolonged operation of cotton picker poses significant risks of work-related musculoskeletal disorders (WMSDs), primarily driven by non-ergonomic cab layouts that fail to accommodate the unique “left-hand steering, right-hand lever” operational mode. Traditional optimization methods, relying on general digital human models or isolated surface electromyography (sEMG) measurements, often lack the physiological fidelity and computational efficiency for high-dimensional, personalized design. To address this interdisciplinary challenge in agricultural engineering and ergonomics, this study proposes a novel AMS-RF-DBO framework that integrates high-fidelity biomechanical simulation with intelligent optimization. A driver–cabin biomechanical model was developed using the AnyBody Modeling System (AMS) and validated against sEMG data (ICC = 0.695). This model generated a dataset linking cab layout parameters to maximum muscle activation (MA). Using steering wheel and control lever coordinates (X, Y, Z) as inputs, a Random Forest (RF) regression model demonstrated strong performance (R² = 0.91). Optimization with the Dung Beetle Optimizer (DBO) algorithm yielded an optimal configuration: steering wheel (L1 = 434 mm, H1 = 738 mm, θ = 32°) and control lever (L2 = 357 mm, H2 = 782 mm, M = 411 mm), reducing upper-limb MA from 3.82% to 1.47% and peak muscle load by 61.5%. This study not only provides empirical support for ergonomic cab design in cotton pickers to reduce operator fatigue and health risks but also establishes a replicable technical paradigm for ergonomic optimization of other specialized agricultural machinery. Full article

Coconut palms (Cocos nucifera L.) are a critical economic and ecological resource in Wenchang City, Hainan. Accurate mapping of their spatial distribution is essential for precision agricultural planning and effective pest and disease management. However, in tropical monsoon regions, persistent cloud cover, spectral similarity with other evergreen species, and redundancy among high-dimensional features hinder the performance of optical classification. To address these challenges, we developed a scalable multi-source remote sensing framework on the Google Earth Engine (GEE) with an emphasis on species-oriented feature design rather than generic feature stacking. The framework integrates Sentinel-1 SAR, Sentinel-2 MSI, and SRTM topographic data to construct a 42-dimensional feature set encompassing spectral, polarimetric, textural, and topographic attributes. Using Random Forest (RF) importance ranking and out-of-bag (OOB) error analysis, an optimal 15-feature subset was identified. Four feature combination schemes were designed to assess the contribution of each data source. The fused dataset achieved an overall accuracy (OA) of 92.51% (Kappa = 0.8928), while the RF-OOB optimized subset maintained a comparable OA of 92.83% (Kappa = 0.8975) with a 64% reduction in dimensionality. Canopy Water Index (CWI), Green Chlorophyll Index (GCI), and VV-polarized backscattering coefficient (${\mathsf{\sigma }}_{\mathrm{V}\mathrm{V}}$) were identified as the most discriminative features. Independent UAV validation (0.07 m resolution) in a 50 km² area of Chongxing Town confirmed the model’s robustness (OA = 90.17%, Kappa = 0.8617). This study provides an efficient and robust framework for large-scale monitoring of tropical economic forests such as coconut palms. Full article

This study attempted to develop and validate a data-driven simulation model that integrates field-measured data to assess the power requirement and fuel consumption characteristics of a self-propelled collector. The collector is a hydrostatic transmission-based, crawler-type platform designed for garlic and onion harvesting, equipped with multiple hydraulic subsystems for collection and sorting. During field experiments, the power requirements of each subsystem and fuel flow rate were recorded, and Willans line method was applied to estimate engine power and subsystem power transmission efficiencies. Because many small agricultural machines do not support electronically instrumented engines (e.g., CAN-bus/ECU-based measurements), the proposed model was formulated as a data-driven, low-order representation derived from on-site measurements rather than a full physics-based model. Using the identified parameters, the simulation framework predicts engine power and fuel efficiency under various operating conditions. The simulation results exhibited high agreement with field data, achieving R² and mean absolute percentage error values of 0.935–0.981 and 1.79–4.18%, respectively, confirming reliable reproduction of real field performance. A comprehensive analysis of the simulation results revealed that both engine speed and travel speed significantly influence power distribution and fuel rate, while also indicating that hydraulic working power is the dominant contributor to total power demand at higher engine speeds. These findings provide practical guidance for improving the fuel efficiency of compact self-propelled collectors. Full article

Banana harvesting relies heavily on manual labor, which is labor-intensive and prone to fruit damage due to insufficient control of contact forces. This paper presents a systematic methodology for the design and optimization of adaptive flexible end-effectors for banana bunch harvesting, focusing on contact behavior and mechanical constraints. By integrating response surface methodology (RSM) with multi-objective genetic algorithm (MOGA) optimization, the relationships between finger geometry parameters and key performance metrics—contact area, contact stress, and radial stiffness—were quantified, and Pareto-optimal structural configurations were identified. Experimental and simulation results demonstrate that the optimized flexible fingers effectively improve handling performance: contact area increased by 13–28%, contact stress reduced by 45–56%, and radial stiffness enhanced by 193%, while the maximum shear stress on the fruit stalk decreased by 90%, ensuring harvesting stability during dynamic loading. The optimization effectively distributes contact pressure, minimizes fruit damage, and enhances grasping reliability. The proposed contact-behavior-constrained design framework enables passive adaptation to fruit morphology without complex sensors, offering a generalizable solution for soft robotic handling of fragile and irregular agricultural products. This work bridges the gap between bio-inspired gripper design and practical agricultural application, providing both theoretical insights and engineering guidance for automated, low-damage fruit harvesting systems. Full article

Efficient water management is increasingly critical in vineyard operations, particularly in the context of climate change and the rising demand for sustainable agricultural practices. Regulated deficit irrigation has emerged as a promising technique that allows significant water savings while sustaining or improving the quality of grapes. However, its effective implementation requires timely and precise information on vine water status and environmental conditions (pluviometry, humidity, radiation, etc.). This study presents the methodology of a decision-support tool that tested the application of several artificial intelligence regression models. Among the algorithms evaluated, an Extreme Gradient Boosting (XGBoost) regression model showed the best performance and was adopted as the core predictive engine of the ViñAI tool to optimize deficit irrigation in vineyards. Based on the developed methodology, the ViñAI tool integrates open-access environmental data such as weather forecasts and satellite-based estimates of evapotranspiration. Furthermore, ViñAI is designed with the potential to integrate sensor-based field data. Overall, the results demonstrate that ViñAI offers a scalable, data-driven approach to support climate-smart irrigation decisions in vineyards, particularly in sensor-sparse or resource-limited contexts, and provides a robust basis for further multi-season and multi-region validation. Full article

Biochar has gained significant attention as a multifunctional material linking biomass energy technologies with sustainable agriculture, providing combined benefits in soil improvement, waste valorization, and climate mitigation. This review examines biochar within the context of thermochemical conversion processes—pyrolysis, gasification, and torrefaction—and summarizes the operational parameters that influence both energy yields and biochar quality. It synthesizes agronomic, environmental, and engineering research to explain the mechanisms through which biochar enhances soil structure, nutrient retention, water availability, microbial activity, and carbon stability. The review also assesses its role as a long-term carbon sink and its potential integration into negative-emission systems such as bioenergy with carbon capture and storage (BECCS). However, the way that biomass conversion factors concurrently influence energy performance, biochar physicochemical quality, and its agronomic and climate-mitigation consequences across many environmental contexts is rarely integrated into a unified analytical framework in current evaluations. To close that gap, this review identifies cross-cutting patterns, trade-offs, and uncertainties while methodically integrating the information on the co-behavior of various aspects. Circular economy initiatives, carbon markets, and rural development are mentioned as key potential. On the other hand, economic variability, variable performance across soil types, lack of regulatory harmonization, rivalry for biomass, and logistical limits are big hurdles. Standardized production techniques, long-term field research, life cycle and techno-economic evaluations, and integrated system design are among the top research priorities. Overall, the evidence suggests that biochar is a promising tool for creating resilient and low-carbon agriculture and energy systems, provided that scientific, technological, and governance advancements are coordinated. Full article

The widespread use of antibiotics in livestock farming has led to the persistent issue of antibiotic residues in meat products, raising significant concerns for food safety and public health. These residues can contribute to the emergence and spread of antimicrobial resistance (AMR), a growing global health threat recognized by the World Health Organization. While some regulatory bodies have imposed restrictions on non-therapeutic antibiotic use in animal agriculture, inconsistent global policies continue to hinder unified efforts to reduce AMR risks. This review explores the role of biotechnology in addressing this challenge by offering innovative tools for the detection, degradation, and removal of antibiotic residues from meat. Biotechnological approaches include the use of biosensors, high-throughput screening, enzymatic degradation, microbial bioremediation, genetically engineered bacteria, phage therapy, and phytoremediation. In addition, enabling technologies such as genomics, metagenomics, bioinformatics, and computational modeling support the rational design of targeted interventions. We further examine the integration of these biotechnological strategies within the broader “One Health” framework, which emphasizes the interconnectedness of human, animal, and environmental health. Case studies and recent applications demonstrate the potential of these methods to ensure safer meat production, reduce public health risks, and enhance consumer trust. By focusing on scalable, science-driven solutions, biotechnology offers a promising path toward mitigating antibiotic residues in the food supply and combating the long-term threat of AMR. Full article

This study introduces an adaptable, intelligent prototype designed to optimize lentil seed germination and biomass accumulation via controlled UV-C radiation and Spirulina supplementation. Building on earlier experiments that separately and jointly assessed these treatments, the work presents a novel seed-treatment chamber that combines environmental sensing, real-time delivery mechanisms, and a machine-learning decision engine. The system automatically selects among three operational modes, Fast Germination, High Biomass, and Flavonoid Enrichment, each targeting a specific agronomic goal. To uncover the most influential treatment factors, the authors applied Analysis of Variance (ANOVA) and Principal Component Analysis (PCA), revealing key response patterns that inform mode definitions. A regression-based AI model was then trained on experimental data to predict treatment outcomes and dynamically adjust parameters. Model performance metrics demonstrate high predictive fidelity, with a Mean Absolute Error (MAE) of 2.1267%, indicating an average deviation of just over two percentage points between predicted and observed germination rates. In comparison, a Mean Squared Error (MSE) of 6.4598 and a corresponding Root Mean Squared Error (RMSE) of 2.5416% confirm consistently low squared deviations. An R² score of 0.8702 indicates that the model accounts for approximately 87% of the variance in germination outcomes, underscoring the robustness of the regression approach. Importantly, the specific treatment ranges illustrated in this study are not direct replications of prior data, but rather representative values drawn from earlier research to demonstrate the framework’s applicability. By abstracting treatment parameters into realistic ranges, the paper shows how the chamber can accommodate various empirical datasets. The principal contribution lies in offering a generalizable methodology for designing AI-enhanced seed-treatment systems. This conceptual framework can be tailored to multiple crops and cultivation environments, paving the way for scalable, precision agriculture solutions that integrate automated monitoring, intelligent control, and real-time optimization. Full article