Search Results (4,264)

Non-destructive prediction of harvest timing is increasingly important in greenhouse melon cultivation, yet image-based methods alone often fail to reflect environmental factors affecting fruit development. Likewise, environmental or fertigation data alone cannot capture fruit-level variation. This gap calls for a multimodal approach integrating both sources of information. This study presents a fusion model combining RGB images with environmental and fertigation data to predict optimal harvest timing for melons. A YOLOv8n-based model detected fruits and estimated diameters under marker and no-marker conditions, while an LSTM processed time-series variables including temperature, humidity, CO₂, light intensity, irrigation, and electrical conductivity. The extracted features were fused through a late-fusion strategy, followed by an MLP for predicting diameter, biomass, and harvest date. The marker condition improved detection accuracy; however, the no-marker condition also achieved sufficiently high performance for field application. Diameter and weight showed a strong correlation (R² > 0.9), and the fusion model accurately predicted the actual harvest date of August 28, 2025. These results demonstrate the practicality of multimodal fusion for reliable, non-destructive harvest prediction and highlight its potential to bridge the gap between controlled experiments and real-world smart farming environments. Full article

The integration of embedded software in multi-sensor wearable devices has revolutionized real-time monitoring across health, fitness, industrial, and environmental applications. This paper presents a comprehensive approach to designing and implementing embedded software architectures that enable efficient, low-power, and high-accuracy data acquisition and processing from heterogeneous sensor arrays. We explore key challenges such as synchronization of sensor data streams, real-time operating system (RTOS) integration, power management strategies, and wireless communication protocols. The reviewed framework supports modular scalability, allowing for seamless incorporation of additional sensors or features without significant system overhead. Future research directions of the embedded software include Hardware-in-the-Loop and real-world validation, on-device machine learning and edge intelligence, adaptive sensor fusion, energy harvesting and power autonomy, enhanced wireless communications and security, standardization and interoperability, as well as user-centered design and personalization. By adopting this focus, we can highlight the potential of the embedded software to support proactive decision-making and user feedback through edge-level intelligence, paving the way for next-generation wearable monitoring systems. Full article

This study examined the influence of short-term olive fruit storage on the quality of virgin olive oil (VOO) from three cultivars (‘Kalinjot’, ‘Leccino’, and ‘Frantoio’) grown in southwest Albania. Olive fruits were processed immediately after harvest, or after 10 days of storage under ambient conditions (20–22 °C) and refrigeration (5 °C). Oils were evaluated for physicochemical quality parameters (free acidity, peroxide value, and UV absorption indices), as well as bioactive and sensory-related compounds (bitterness index, chlorophylls, carotenoids, and total phenolic content). Results showed that immediate processing yielded the highest quality oils, with low free acidity (0.28–0.35%) and preserved bioactive compounds. Ambient storage led to marked deterioration, including significant increases in free acidity and peroxide values, loss of pigments, and 20–70% reduction in phenolic content, accompanied by decreased bitterness. In contrast, cold storage mitigated these effects, maintaining values closer to baseline and preserving sensory and functional attributes. ANOVA confirmed significant effects of storage duration, temperature, and cultivar on most parameters, with ‘Kalinjot’ exhibiting greater stability compared to ‘Frantoio’ and ‘Lecino’. These findings highlight that minimizing the interval between harvest and milling is critical for ensuring oil quality, while refrigerated storage offers a practical strategy to safeguard chemical and sensory characteristics when immediate processing is not feasible. Full article

Photovoltaic generation systems (PVGSs) face significant efficiency challenges under partial shading conditions and rapidly changing irradiance due to the limitations of conventional maximum power point tracking (MPPT) methods. To address these challenges, this paper proposes a Transfer Learning-based Fractional-Order Recurrent Neural Network (TL-FRNN) for robust global maximum power point (GMPP) tracking across diverse operating conditions. The incorporation of fractional-order dynamics introduces long-term memory and non-local behavior, enabling smoother state evolution and improved discrimination between local and global maxima, particularly under weak and partially shaded conditions. The proposed approach leverages Caputo fractional derivatives with Grünwald–Letnikov approximation to capture the history-dependent behavior of PVGSs while implementing a parameter-partitioning strategy that separates shared features from task-specific parameters. The architecture employs a multi-head design with GMPP regression and partial shading classification capabilities, trained through a two-stage process of pretraining on general PV data followed by efficient fine-tuning on target systems with limited site-specific data. The TL-FRNN achieved 99.2% tracking efficiency with 98.7% GMPP detection accuracy, reducing convergence time by 53% compared to state-of-the-art alternatives while requiring 72% less retraining time through transfer learning. This approach represents a significant advancement in adaptive, intelligent MPPT control for real-world photovoltaic energy-harvesting systems. Full article

Tendon injuries, whether resulting from trauma, repetitive strain, or degenerative conditions, present a considerable clinical challenge. The natural healing process, which involves inflammatory, proliferative, and remodeling phases, is often inefficient and leads to excessive scar tissue formation, ultimately compromising the mechanical properties of the tendon compared to its native state. This highlights the critical need for innovative approaches to enhance tendon repair and regeneration. Leveraging the regenerative properties of human amniotic membrane (HAM) and electrospun PCL/gelatin nanofibers, this study aims to develop and assess a novel composite scaffold in a rodent model to facilitate improved tendon healing. This prospective experimental study involved 12 male Sprague Dawley rats (250–300 g), randomly assigned to three groups: Group A (No Treatment/No HAM), Group B (HAM-treated), and Group C (HAM with electrospun nanofibers, HAM-NF). A surgically induced tendon injury was created in the left hind limb, while the right limb served as a control. Following surgery, HAM and HAM-NF (0.5 cm²) were applied to the respective treatment groups, and tendon healing was assessed after six weeks. Gait analysis, including stride length and toe-out angle, was conducted both pre-operatively and six weeks post-operatively. Macroscopic and microscopic evaluations were performed on harvested tendons to assess regeneration, comparing treated groups to the controls. Gait analysis demonstrated that the HAM-NF group showed a significant increase in stride length from 11.70 ± 1.50 cm to 12.79 ± 1.71 cm (p < 0.05), with only a modest change in toe-out angle (14.58 ± 2.96° to 16.27 ± 2.20°). In contrast, the No Treatment group exhibited reduced stride length (10.27 ± 2.17 cm to 8.40 ± 1.67 cm) and a marked increase in toe-out angle (16.33 ± 4.51° to 26.47 ± 5.81°, p < 0.05), while the HAM-only group showed mild changes in both parameters. Macroscopic evaluation showed a significant difference in tendon healing. HAM-NF group had the highest score that indicates more rapid tissue regeneration. Histological analysis after 6 weeks showed that tendons treated with HAM-NF achieved a mean histological score of 5.54 ± 4.14, closely resembling the uninjured tendon (6.67 ± 1.63), indicating substantial regenerative potential. The combination of human amniotic membrane (HAM) and electrospun nanofibers presents significant potential as an effective strategy for tendon regeneration. The HAM/NF group exhibited consistent improvements in gait parameters and histological outcomes, closely mirroring those of uninjured tendons. These preliminary results indicate that this biomaterial-based approach can enhance both functional recovery and structural integrity, providing a promising pathway for advanced tendon repair therapies. Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article

This study evaluated the effects of nitrogen fertilization and different types of planting material on the yield and fruit quality of pineapple (Ananas comosus var. comosus) cv. Smooth Cayenne under the edaphoclimatic conditions of the Northern region of Rio de Janeiro State, Brazil. The experiment was conducted in a randomized block design, arranged in a factorial scheme with four nitrogen rates, six types of planting material, and two harvest seasons (winter and summer). Based on the results, it can be inferred that slips provided higher yields and heavier fruits, whereas plants derived from crowns and suckers showed lower productivity. Increasing nitrogen rates promoted greater fruit mass and length, higher pulp percentage, and increased production of vegetative propagules. Fruits harvested in the summer showed higher soluble solids content (15.5 °Brix), greater pulp and juice percentages, and lower titratable acidity, which are desirable characteristics for fresh consumption. Despite the seasonal differences, fruit mass ranging from 1.5 to 2.0 kg met commercial standards for both processing and domestic markets. The soluble solids/titratable acidity ratio (15.8) was below the ideal range for fresh consumption. The combination of appropriate planting material and nitrogen fertilization contributes to higher production efficiency, cost reduction, and improved fruit quality. Full article

The essential oil (EO) of Mentha spicata L. (spearmint) exhibits pronounced biological activity, making it valuable for applications in agrochemistry as an insecticidal agent, in perfumery and cosmetics, and as a natural preservative in the food industry. However, maintaining the integrity and yield of EO during post-harvest processing and extraction remains a key technological challenge. This study aimed to enhance the vacuum-drying (VD) process of spearmint using calcium chloride as a desiccant and to optimize the conditions of supercritical CO₂ extraction (SC-CO₂), including EO separation and the evaluation of its solubility under dynamic extraction conditions. The incorporation of calcium chloride into the VD process reduced drying duration by 21.1% and processing costs by 31.0%, while increasing EO yield by 11%. A decrease in separator pressure from 70 to 10 bar during SC-CO₂ extraction resulted in nearly a threefold increase in EO yield by minimizing the loss of volatile constituents. The solubility of spearmint EO in supercritical CO₂ was successfully described by the Chrastil model and correlated with carvone solubility. The maximum total phenolic content (72.3 ± 2.2 mg gallic acid equivalent per gram) was observed at a CO₂ density of 353.91 kg/m³. The solubility of EO was studied directly using the plant matrix under dynamic conditions. Full article

Hydroponic leafy vegetables have high yields, but their mechanized harvesting level is low. To improve the quality of orderly harvesting of hydroponic leafy vegetables, an ordered harvesting device with functions such as root flicking, posture adjustment and differential steering has been designed. A root-flicking method using rotating brushes was proposed, and experiments analyzed the effect of brush rotation direction. A posture adjustment method using a twisting conveyor belt was proposed, and experiments analyzed the impact of different twist angles on the stability of the adjustment process and the posture angle. A differential steering method using two pairs of conveyor belts with differential speeds was proposed, and experiments analyzed the impact of different steering belt speeds on steering stability and actual inclination angle. The root-flicking experiment results for hydroponic leafy vegetables showed that the counterclockwise followed by clockwise brush rotation combination performed better, with a root removal rate of 74.9%. The posture adjustment experiments on hydroponic leafy vegetables showed that the optimal twist angle for clamped and twisted conveyance was 35°, and the Mean Absolute Error (MAE) between the twist angle and hydroponic leafy vegetables posture angle was 0.38°. The differential steering experiment results indicated that the preferable belt speed difference for the directional conveyance of hydroponic leafy vegetables was 75 mm/s, and the MAE between the theoretical and actual inclination angles was 0.96°. Validation experiments based on posture adjustment and differential steering tests demonstrated a continuous harvesting success rate of 87.5%. This study provides valuable references for the design and development of orderly harvesting equipment for hydroponic leafy vegetables. Full article

In recent times, field crop damage caused by birds, such as corvids and pigeons, has become crucial for many farmers. Damage can be as serious as the loss of a large part of the harvest. Several solutions have been proposed, but none are effective. An example is the use of scarecrows, but birds eventually adapt to them over time, and so they become ineffective. To study bird behavior and to propose a bird deterrent that would adapt to the presence of birds, we set up an experimental image-taking system on several plots of land over a period of 4–5 years. Around fifteen terabytes of images taken in the field were acquired. Our aim was to automatically detect these birds using deep learning methods and then to activate a real-time scarer. This work meets two challenges: the first is agroecological, as bird damage has become a major issue, and the second is IT, as it is difficult to detect birds in the field: the individuals are small because they are far from the camera lens, and field conditions are often less than optimal: darkness, confusion between the pigeons’ colors and the ground, etc. The Mask-RCNN in its original configuration is not suited to detecting small individuals. We mainly focused on the model’s hyperparameters to better adapt it to our study context. As a result, we improved the detection of small individuals using, among other things, appropriate anchor scales design and image processing techniques. At the same time, we built an original dataset focused on small individuals called BirdyDataset. The model can detect corvids and pigeons with an accuracy of 78% under real field conditions. Full article

Morningglories (Ipomoea lacunosa, I. hederacea, and I. purpurea) are persistent, problematic weeds in summer row crops throughout warm-temperate regions. Their vining growth habit and enduring seedbanks lead to recurring infestations and harvest interferences. This review synthesizes current knowledge on the seed ecology of these species to clarify how dormancy, germination, and emergence processes contribute to their persistence. Published anatomical and ecological studies were examined to summarize dormancy mechanisms, environmental signals regulating dormancy release, germination requirements, and seasonal emergence patterns. Morningglories exhibit a dormancy system dominated by physical dormancy, occasionally combined with a transient physiological component. Dormancy release is promoted by warm and fluctuating temperatures, hydration–dehydration cycles, and long-term seed-coat weathering. Once permeable, seeds germinate across broad temperature ranges, vary in sensitivity to water potential, and show limited dependence on light. Field studies indicate extended emergence windows from late spring through midsummer, especially in no-till systems where surface seeds experience strong thermal and moisture fluctuations. Despite substantial progress, significant gaps remain concerning maternal environmental effects, population-level variation, seedbank persistence under modern management, and the absence of mechanistic emergence models. An improved understanding of these processes will support the development of more predictive and ecologically informed management strategies. Full article

Drought, high temperature, salinity, waterlogging, and nutrient deficiency, along with metal toxicity, are among the environmental factors that have resulted in much alteration of many ecosystems by climate change. Such stresses have dramatically lowered the global average human harvest of core crops, which, in turn, has driven an overall decrease in worldwide agricultural productivity. Plants have developed a variety of defense strategies against biotic and abiotic stress. Evidence of the successful roles of phytohormone-like neurotransmitters in ameliorating the response to stress has already been established. One neurotransmitter accumulated by the plants is gamma-aminobutyric acid (GABA), a non-protein amino acid that is essential for signaling in plant growth regulation and development via the control of physiological and biochemical processes. Plant tissues demonstrate rapid accumulation of GABA when exposed to various abiotic stresses. Consequently, it is imperative to understand how this accumulation affects the resistance and productivity of crops in challenging environmental conditions. Previously, different application methods and doses of GABA on different plant species were used under various abiotic stress conditions. The research findings exhibited that the method and concentration of GABA depend on the type of crop. Furthermore, the GABA dose depends on the methods of GABA application. The present review summarizes the potential doses and methods of applications of GABA under different abiotic stress conditions to ameliorate deficiencies in plant growth, yield, and stress tolerance through the avoidance of oxidative damage and maintenance of cell organelle structures. This review will also describe the complex mechanism by which GABA contributes to the attenuation of the effects of abiotic stresses by regulating some important physiological, molecular, and biochemical processes in crops. Full article

The growing need for self-powered Internet of Things networks has raised interest in converting abundant waste into reliable energy harvesters despite long-standing material and technology challenges. As demand for environmentally friendly self-powered IoT devices continues to rise, attention toward green waste as an eco-friendly energy source has strengthened. However, its direct utilisation in high-performance energy harvesters remains a significant challenge. Driven by the growing need for renewable sources, the triboelectric nanogenerator has emerged as an innovative technology for converting mechanical energy into electricity. In this work, the design, fabrication, and characterisation of a rotating triboelectric energy harvester as a prototype device employing date seed waste as the tribopositive layer are presented. The date seeds particles, measuring 1.2 to 2 mm, were pulverised using a grinder, mixed with epoxy resin, and subsequently applied to the grating-disc structure. The coated surface was machined on a lathe to provide a smooth surface facing. The performance of the prototype was evaluated through a series of experiments to examine the effects of rotational speed, the number of grating-disc structures, the epoxy mixing process, and the prototype’s influence on the primary system, as well as to determine the optimal power output. An increase in rotational speed (RPM) enhanced power generation. Furthermore, increasing the number of gratings and pre-mixing of epoxy with the biomaterial resulted in enhanced output power. Additionally, with 10 gratings, operating at 1500 rpm, and a 24 h pre-mixing method, the harvester achieved maximum voltage and power outputs of 129 volts and 1183 μW at 7 MΩ. Full article

This study addresses the lack of transparent methods for calculating the energy requirements of pulsed electric field (PEF) pretreatments in biogas research. Two detailed approaches are proposed and evaluated to quantify the energy consumed during the pretreatment of lignocellulosic harvest residues (corn, soybean, and sunflower) using a low-frequency electric field. The first approach is based on previously measured capacitor parameters, including resistance (R_s, R_p), inductance (L_s), capacitance (C_p), and loss factor (D), which were interpolated to 50 Hz from measurements performed over the frequency range of 100 Hz to 10 kHz. The second approach relies on direct measurements of the effective voltage and current waveforms across the capacitor, followed by calculation of the power factor (cos φ). Both methods enable reliable estimation of energy consumption and differ primarily in the type of input data required: Method 1 is based on capacitor characteristics determined before and after pretreatment, while Method 2 uses real-time treatment data. Despite these differences, the two approaches yielded highly consistent results, confirming their robustness and applicability. The calculated energy values were subsequently incorporated into a net energy balance by comparing the energy consumed during pretreatment with the methane energy output from anaerobic digestion. For all three investigated lignocellulosic substrates, PEF pretreatment resulted in a positive energy balance under the applied process conditions. Full article

Mango is the second most widely cultivated tropical fruit in the world. Its harvesting mainly relies on manual labor. During the harvest season, the hot weather leads to low working efficiency and high labor costs. Current research on automatic mango harvesting mainly focuses on locating the fruit stem harvesting point, followed by stem clamping and cutting. However, these methods are less effective when the stem is occluded. To address these issues, this study first acquires images of four mango varieties in a mixed cultivation orchard and builds a dataset. Mango detection and occlusion-state classification models are then established based on YOLOv11m and YOLOv8l-cls, respectively. The detection model achieves an AP_0.5–0.95 (average precision at IoU = 0.50:0.05:0.95) of 90.21%, and the accuracy of the classification model is 96.9%. Second, based on the mango growth characteristics, detected mango bounding boxes and binocular vision, we propose a spatial localization method for the mango grasping point. Building on this, a mango-grasping and stem-cutting end-effector is designed. Finally, a mango harvesting robot system is developed, and verification experiments are carried out. The experimental results show that the harvesting method and procedure are well-suited for situations where the fruit stem is occluded, as well as for fruits with no occlusion or partial occlusion. The mango grasping success rate reaches 96.74%, the stem cutting success rate is 91.30%, and the fruit injury rate is less than 5%. The average image processing time is 119.4 ms. The results prove the feasibility of the proposed methods. Full article