Search Results (440)

Energy harvesting systems (EHSs) are widely used to power wireless sensors. Piezoelectric harvesters have the advantage of producing an electric signal directly related to the exciting force and can thus be used to power condition monitoring sensors in dynamically loaded structures such as bridges. The need for such monitoring is exemplified by the fact that the condition of close to 25% of public roadway bridges in, e.g., Germany is not satisfactory. Stack-based piezoelectric energy harvesting systems (pEHSs) installed near bridge bearings could provide information about the traffic and dynamic loads on the one hand and condition-dependent changes in the bridge characteristics on the other. This paper presents an approach to co-optimizing the design of the mechanical and electrical components using a nonlinear solver. Such an approach has not been described in the open literature to the best of the authors’ knowledge. The mechanical excitation is estimated through a finite element simulation, and the electric circuitry is modeled in Simulink to account for the nonlinear characteristics of rectifying diodes. We use real traffic data to create statistical randomized scenarios for the optimization and statistical variation. A main result of this work is that it reveals the strong dependence of the energy output on the interaction between bridge, harvester, and traffic details. A second result is that the methodology yields design criteria for the harvester such that the energy output is maximized. Through the case study of an actual middle-sized bridge in Germany, we demonstrate the feasibility of harvesting a time-averaged power of several milliwatts throughout the day. Comparing the total amount of harvested energy for 1000 randomized traffic scenarios, we demonstrate the suitability of pEHS to power wireless sensor nodes. In addition, we show the potential sensory usability for traffic observation (vehicle frequency, vehicle weight, axle load, etc.). Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R² = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R² = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article

Traditional rainfed fig orchards intended for fresh consumption tend to have low yields and cultural practices difficulties due to wide plant spacing and large canopies. This study investigates whether the espalier training system, commonly employed in other fruit species, can be applied to fig cultivation to improve productivity and fruit quality under high-density irrigated plantations. For the first time, four fig varieties (‘San Antonio’, ‘Dalmatie’, ‘Albacor’, and ‘De Rey’) were evaluated in a high-density system (625 trees/ha) using espalier training over four consecutive years (2018–2021) in southwestern Spain. Among the varieties, ‘Dalmatie’ demonstrated the highest suitability to the system, combining low vegetative vigour with superior yield performance, reaching a cumulative yield of 103.15 kg/tree and yield efficiency of 1.94 kg/cm². ‘San Antonio’ was the earliest to ripen and exhibited the longest harvest duration (81 days), enabling early and extended market availability. In terms of fruit quality, ‘Albacor’ stood out for its high total soluble solids content (24.97 °Brix), while ‘De Rey’ exhibited the best sugar–acid balance, with a maturity index of 384.58. The present work demonstrates that intensive fig cultivation on espalier structures offers an innovative alternative to traditional systems, thereby enhancing orchard efficiency, management, and fruit quality. Full article

This study investigates the impact of hydroxytyrosol (HTyr) supplementation on the fatty acid profiles and oxidative stability of various extra virgin olive oil (EVOO) cultivars and other edible oils during prolonged deep-frying. EVOO cultivars including Picual, Cornicabra, Empeltre, Arbequina, Hojiblanca, Manzanilla, Royuela, Koroneiki, and Arbosana were analyzed alongside two sunflower oils and three refined olive oils under thermal stress at 170–210 °C for 3–6 h. HTyr consistently preserved monounsaturated fatty acids (MUFAs), particularly oleic acid (C18:1), while significantly reducing the degradation of polyunsaturated (PUFAs) and saturated fatty acids (SFAs) (p < 0.05) in many oil samples; for example, in olive oil °1, TMUFAs in Exp 1 revealed 7.28%, while in Exp 5 (with HTyr), TMUFAs increased to 7.47%. In olive oil °0.4, TMUFAs increased from 8.52% in Exp 1 to 9.17% in Exp 5. Additionally, In EVOO cv. Picual, total SFAs increased slightly, from 16.58% in Exp 1 to 16.96%, in Exp 5. Notably, total MUFA content (TMUFAs) was best preserved in Manzanilla (81.92%), followed by Hojiblanca (78.52%), Empeltre (78.09%), olive oil 1° (78.20%), Koroneiki (77.60%), and Arbosana (77.01%) (p < 0.05), indicating strong oxidative resistance. In Arbequina and Royuela oils, oleic acid retention also exceeded 76% after deep-frying. HTyr helped maintain fatty acid profiles within EU regulatory limits across most cultivars, despite minor exceedances in specific SFAs, such as lignoceric acid (C24:0), likely due to varietal traits or harvest timing. Principal component analysis (PCA) revealed distinct clustering patterns: sunflower oils grouped around linoleic acid (C18:2), reflecting high PUFA content, while olive oils clustered near oleic and palmitic acids. Cultivars such as Picual, Empeltre, Manzanilla, and Royuela showed unique associations with lignoceric acid, supporting the use of fatty acid profiles as cultivar-specific markers. HTyr supplementation enhanced oxidative stability and quality retention across oil types in terms of fatty acids profile, corroborating previous findings on the resilience of polyphenol-rich EVOOs under thermal stress. Furthermore, fatty acid composition varied significantly according to cultivar, HTyr, and deep-frying (p < 0.05), highlighting the complexity of oil quality determinants. This study supports the application of HTyr as a natural antioxidant to improve thermal stability and nutritional quality, not only in olive oils but also in other edible oils. These findings promote sustainable practices aligned with circular economy principles and advance the understanding of fatty acid dynamics during deep-frying. HTyr-enriched oils present promising potential in both culinary and industrial contexts. Full article

The tropical house cricket (Gryllodes sigillatus) can convert organic diets formulated from weeds and agro by-products into high-quality biomass. This study assessed the potential of diets developed from weeds and agro by-products as a feed source for G. sigillatus. We compared the development and nutritional value of crickets fed these alternative diets with control crickets fed chicken feed. Ten different diets with varying protein contents were used, including chicken feed (Control) with a protein content of 215 g/Kg dry matter (DM) basis), Cassava–Sugar Diet (250 g/Kg DM protein) Desmodium–Bran Diet (245 g/Kg DM protein), Morning Glory–Bean Diet (240 g/Kg DM protein), Morning Glory–Cassava Diet (235 g/Kg DM protein), Morning Glory–Cowpea Diet (225 g/Kg DM protein), Mixed Weed–Bran Diet (Optimal) (215 g/Kg DM protein) Cassava–Gallant Soldier Diet (200 g/Kg DM protein), Wheat–Bran Diet (145 g/Kg DM protein), and Maize–Cassava Diet (135 g/Kg DM protein). The weight and length of the crickets were measured for 9 weeks from day 1 after hatching to day 56. Then, the crickets were harvested and analyzed for dry matter, crude protein, fat, ash, fiber, minerals, and fatty acid composition. Cricket developmental time, survival rate, weight and length, yield, proximate components, and mineral and fatty acids differed depending on the diet provided. The Mixed Weed–Bran Diet (Optimal) resulted in the crickets developing faster (48.8 days), with a higher survival rate (88.1%), greater adult length (19.2 cm) and weight (0.44 g), and a nutrition content richer in minerals and unsaturated fatty acids when compared to other treatments. Oleic, linoleic, and palmitic acids were the major fatty acids. The highest protein content (64.4 g/100 g) was observed in the Mixed Weed–Bran Diet (Optimal) and Morning Glory–Cassava Diet treatments, while the Maize–Cassava Diet treatment crickets possessed the highest quantities of fats (19.1 g/100 g) and ash (15.4 g/100 g). The fatty acid profile of G. sigillatus revealed the cricket to have high unsaturated fatty acids except in crickets fed Morning Glory–Cowpea Diet and Wheat–Bran Diet. Generally, G. sigillatus grew best and had the most nutritious body composition on the Mixed Weed–Bran Diet (Optimal). The findings indicate that diets developed from weeds and agro by-products have great potential to be used as an alternative feed source for crickets and are capable of replacing expensive chicken feed, enhancing the circular farming potential of insect farming. Full article

This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R²P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R²P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R² = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling. Full article

Moldy core is a common disease of apples, and non-destructive, rapid and accurate detection of moldy core apples is essential to ensure food safety and reduce post-harvest economic losses. In this study, the acoustic method was used for the first time for the online detection of moldy core apples, and we explore the feasibility of integrating acoustic and visible–near-infrared spectroscopy (Vis–NIRS) technologies for precise, real-time detection of moldy core in apples. The sound and Vis–NIRS signals of apples were collected using a novel acoustic online detection device and a traditional Vis–NIRS online sorter, respectively. Based on this, traditional machine learning and deep learning classification models were developed for the prediction of healthy, mild, moderate, and severe moldy apples. The results show that the acoustic detection method significantly outperforms the Vis–NIRS method in terms of moldy apple identification accuracy, and the fusion of acoustic and Vis–NIRS data can further improve the model prediction performance. The MLP-Transformer shows the best prediction performance, with the overall classification accuracies for the fusion of Vis–NIRS, acoustic, Vis–NIRS and acoustic reached 89.66%, 96.55%, and 98.62%, respectively. This study demonstrates the excellent performance of acoustic online detection for intra-fruit lesion identification and shows the potential of the fusion of acoustics and Vis–NIRS. Full article

Green roofs (GRs) are increasingly implemented for stormwater management, and retrofitting conventional roofs is emerging as a key strategy for climate change resilience. However, their impact on diffuse pollution, particularly regarding total organic carbon (TOC) and pollutant mass transport, remains insufficiently understood, especially in aged substrates. This study evaluated and compared the runoff quality from aged GRs and ceramic roofs (CRs) by analyzing TOC, pH, electrical conductivity (EC), first-flush occurrence and intensity, and pollutant release patterns. Results showed that GR retrofitting could help mitigate acid-rain effects due to its elevated pH. Despite higher TOC and EC concentrations in runoff, GRs remained within acceptable water quality limits and exhibited a more gradual release of organic matter over time compared with CRs. Statistical analysis revealed that pollutant concentrations in CR runoff followed Lognormal and Weibull distributions, while GR runoff was best described by Normal, Lognormal, and Weibull distributions. These findings reinforce GRs as a viable stormwater management strategy but highlight the need for full runoff treatment when used for rainwater harvesting. The results also emphasize the importance of tailored statistical models to enhance runoff predictions and optimize GR performance in urban water management. The results provide valuable insights for urban planners and policymakers by reinforcing the potential of GRs in stormwater quality management and supporting the development of incentives for green infrastructure. Future research should expand to different GR configurations, climates, and maintenance practices to enhance the understanding of long-term hydrological and water quality performance. Full article

Virginia mallow or Sida hermaphrodita (L.) Rusby (SH) is a perennial plant from the Malvaceae family (mallows) that is used for medicinal purposes, reducing soil erosion, cleaning soil, and most recently for energy production. The potential of sustainable lignocellulosic agro-waste is immense as it represents Earth’s most abundant organic compound. This paper explores fibers isolated from SH stems, a plant with significant industrial application potential, including technical textiles and biocomposites. The fibers were harvested in January, March, and November of 2020 and in January and March of 2021, and their yield, mechanical properties, moisture content, and density were thoroughly analyzed. The fiber yield showed slight variations depending on the harvest time, with consistent results observed across different years, suggesting stable productivity. The SH fibers demonstrated a favorable moisture content, making them suitable for storage and processing, and their density ranged between 1.52 and 1.58 g/cm³, comparable to that of other natural fibers. According to this research, the best mechanical properties were observed in the winter harvest. Furthermore, the high percentage of solid residue left after fiber extraction shows promise for sustainable utilization, primarily for biofuel production. This study underscores the versatility and sustainability of SH fibers, positioning them as a valuable resource for a wide range of industrial applications. Full article

In citriculture, inputs like water and fertilizer are applied through traditional basin methods, thereby incurring reduced use-efficiency. The response of conventional crop coefficient-based fertigation scheduling continues to be inconsistent and complex in its field implementation, thereby necessitating the intervention of sensor-based (Internet of Things; IoT) technology for fertigation scheduling on a real-time basis. The study aimed to investigate fertigation scheduling involving four levels of irrigation, viz., I₁ (100% evapotranspiration (ET) as the conventional practice), I₂ (15% volumetric moisture content (VMC)), I₃ (20% VMC), and I₄ (25% VMC), as the main treatments and three levels of recommended doses of fertigation, achieved by reappropriating different nutrients across phenologically defined critical growth stages, viz., F₁, F₂, and F₃ (conventional fertilization practice), as sub-treatments, which were evaluated through a split-plot design over two harvesting seasons in 2021–2023. Nagpur mandarin (Citrus reticulata Blanco) was used as the test crop, which was raised on Indian Vertisol facing multiple nutrient constraints. Maximum values for physiological growth parameters (plant height, canopy area, canopy volume, and relative leaf water content (RLWC)) and fruit yield (characterized by 9% and 5%, respectively, higher A-grade-sized fruits with the I₄ and F₁ treatments over corresponding conventional practices, viz., I₁ and F₃) were observed with the I₄ irrigation treatment in combination with the F₁ fertilizer treatment (I₄F₁). Likewise, fruit quality parameters, viz., juice content, TSS, TSS: acid ratio, and fruit diameter, registered significantly higher with the I₄F₁ treatment, featuring the application of B at the new-leaf initiation stage (NLI) and Zn across the crop development (CD), color break (CB), and crop harvesting (CH) growth stages, which resulted in a higher leaf nutrient composition. Treatment I₄F₁ conserved 20–30% more water and 65–87% more nutrients than the I₁F₃ treatment (conventional practice) by reducing the rate of evaporation loss of water, thereby elevating the plant’s available nutrient supply within the root zone. Our study suggests that I₄F₁ is the best combination of sensor-based (IoT) irrigation and fertilization for optimizing the quality production of Nagpur mandarin, ensuring higher water productivity (WP) and nutrient-use-efficiency (NUE) coupled with the improved nutritional quality of the fruit. Full article

Crop rotation affects soil pH by disturbing H⁺ production and consumption within soil–crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil pH prediction, because the spatial variability in soil pH is lower and the way crop rotation influences pH is different. To quantify the extent to which crop rotation improves soil pH mapping, we introduced the strategy of grouping soil samples by crop rotation and modeling separately. We chose a typical multiple-cropping region suffering soil acidification in Southern China, where the complex crop rotation was mapped by Sentinel-1/2 time series and a legend featuring three main systems (i.e., paddy, vegetable, and orchard) and nine subsystems. This crop rotation map was then combined with other variables to derive multiple combinations and predict soil pH. Based on the best combination, we further assessed the grouping strategy. The results showed that simply incorporating crop rotation in one joint model was useful but could not obtain the expected accuracy, with a root mean squared error (RMSE) of 0.66 and an R² of 0.36. The individual statistical accuracies were quite low for the vegetable and orchard rotations, with an RMSE of 0.77/0.70 and an R² of 0.30/−0.04. Grouping soil samples by crop rotation significantly enhanced soil pH predictability with a decrease in the RMSE of 15% and an increase in the R² of 53%. The results proved that grouping by crop rotation can fit and optimize the sub-models after learning the characteristics of the rotation subsamples, offering a way for improving digital mapping of soil pH over heterogeneous agricultural landscapes. Full article

Acacia species are important trees in arid ecosystems due to their diverse ecological roles, such as providing vegetation cover, community structures, food resources for animals, soil stabilization, and erosion prevention. However, in the Arabian Peninsula, Acacia species are declining due to climate change, overgrazing, and fuelwood harvesting. This study evaluates the effectiveness of various pre-sowing treatments—sulfuric acid soaking and tap and hot water soaking—on breaking seed dormancy to enhance germination in nine Acacia species native to the Al-Baha region of Saudi Arabia. The key germination indicators assessed were the mean germination time (MGT), germination percentage (GP), and germination index (GI). Sulfuric acid treatments for 10–15 min reduced the MGT and increased the GP for A. etbaica, A. hamoulosa, and A. tortilis, while A. origena responded best to 1 min of hot water soaking. Conversely, A. asak, A. ehrenbergiana, and A. johnwoodii showed little to no germination improvement with treatment and A. oerfota and A. gerrardii showed no germination improvement, indicating the need for alternative methods. These findings indicate that the seed germination requirements vary within Acacia spp. from the same geographic region and similar climatic conditions. Further work is required for five of the species tested to develop better seed germination techniques, given the potential utility of Acacia spp., in ecological restoration and sustainable land management in arid regions. Full article

Agriculture needs to produce more with fewer resources to satisfy the world’s demands. Labor shortages, especially during harvest seasons, emphasize the need for agricultural automation. However, the high cost of commercially available robotic manipulators, ranging from EUR 3000 to EUR 500,000, is a significant barrier. This research addresses the challenges posed by low-cost manipulators, such as inaccuracy, limited sensor feedback, and dynamic uncertainties. Three control strategies for a low-cost agricultural SCARA manipulator were developed and benchmarked: a Sliding Mode Controller (SMC), a Reinforcement Learning (RL) Controller, and a novel Proportional-Integral (PI) controller with a self-tuning feedforward element (PIFF). The results show the best response time was obtained using the SMC, but with joint movement jitter. The RL controller showed sudden breaks and overshot upon reaching the setpoint. Finally, the PIFF controller showed the smoothest reference tracking but was more susceptible to changes in system dynamics. Full article

The feasibility of an analogous reverse electrodialysis (RED) process for power generation and acid recovery from acidic waste streams in the steel industry is investigated in this study. A comprehensive model was established to simulate the transport phenomena and power generation, which was validated through experimental data. The simulated operation time was 3 h, during which an acid recovery rate of 41.7% was achieved, and the maximum output power density reached 30.37 μW·cm⁻². The results demonstrated a strong dependence of output power density on the acid concentration, with a linear relationship within the tested range of 1.0–3.0 mol·L⁻¹ HCl. An optimal flow rate range was identified that maximized power output, with the best value of 90 mL∙min⁻¹. The differences in energy harvesting between the traditional acid diffusion dialysis process and our analogous RED process were demonstrated via simulation. The importance of system electroneutrality in driving ion migration and forming ionic currents was crucial for effective power generation. The analogous RED process is a promising solution for efficient acid recovery and power generation from industrial acid waste, offering a sustainable treatment approach. Full article