Search Results (1,987)

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article

In this study, the impact of moderate and high CO₂ and O₂ levels was compared to low and moderate gas combinations during prolonged storage on the quality of Regina sweet cherries harvested in different maturity stages, particularly in terms of decreasing internal browning. Fruits were harvested in two different maturity stages (Light and Dark Mahogany skin color) and stored in CA of 15% CO₂ + 10% O₂; 10% CO₂ + 10% O₂; 10% CO₂ + 5% O₂; 5% CO₂ + 5% O₂ and MA of 4 to 5% CO₂ + 16 to 17% O₂ for 30 and 40 days at 0 °C and 90% RH, followed by a marketing period. After the storage, both maturity stages significantly reduced internal browning, decay, and visual quality losses in CA with 10–15% CO₂ and 10% O₂. In addition, it preserved luminosity, total soluble solids (TSSs), titratable acidity (TA), and bioactive compounds such as anthocyanins and phenols. This treatment also maintained the visual appearance of the sweet cherries, favoring their market acceptance. At the same time, the light red fruits showed a better general quality compared to darker color after the storage. In conclusion, a controlled atmosphere with optimized CO₂ and O₂ concentrations, together with harvesting with a Light Mahogany external color, represents an effective strategy to extend the shelf life of Regina sweet cherries up to 40 days plus the marketing period, maintaining their physical and sensory quality for export markets. Full article

Drought stress is a major environmental constraint that significantly reduces wheat productivity worldwide. In this study, seventeen wheat genotypes were evaluated under well-watered and drought-stressed conditions across two consecutive years (2023–2024) in a controlled greenhouse experiment. Twenty morphological and agronomic traits were recorded, and their responses to prolonged water limitation were assessed using multivariate statistical methods, including three-way ANOVA, principal component analysis (PCA), and cluster analysis. Drought stress significantly decreased all traits except the harvest index (HI), with the most severe reductions observed in traits related to secondary spikes (e.g., grain weight reduced by 95%). The ANOVA results confirmed significant genotype × treatment (G × T) interactions for key agronomic traits, with the strongest effect observed for total grain weight (F = 7064.30, p < 0.001). A PCA reduced the 20 original variables to five principal components, explaining 87.2% of the total variance. These components reflected distinct trait groups associated with productivity, spike architecture, and development in phenology. Cluster analysis based on PCA scores grouped genotypes into three clusters with contrasting drought response profiles. A yield-based evaluation confirmed the cluster structure, distinguishing genotypes with a stable performance (average yield loss ~58%) from highly sensitive ones (~70% loss). Overall, the findings demonstrate that drought tolerance in wheat is governed by complex trait interactions. Integrating a trait-based multivariate analysis with a yield stability assessment enables the identification of genotypes with superior adaptation to water-limited environments, providing an excellent genotype background for future breeding efforts. Full article

This study explores the development of a topical film-forming spray infused with phytobiotic herbs to extend egg shelf life and maintain its quality. Unlike traditional surface treatments, film-forming sprays provide uniform drug distribution, better bioavailability, effective CO₂ retention by sealing pores, and antibacterial effects. The spray includes a polymer to encapsulate phytoconstituents and form the film. The resulting film is highly water-resistant, glossy, transparent, and dries within two minutes. SEM analysis showed a fine, uniform morphology, while zeta analysis revealed a negative potential of −0.342 mV and conductivity of 0.390 mS/cm, indicating stable dispersion. The spray’s effectiveness was tested on 640 chicken eggs stored at varying temperatures. Eggs treated and kept at 2–8 °C showed the best results, with smaller air cells, higher specific gravity, and superior quality indicators such as pH, albumen weight, albumen height and index, Haugh unit, yolk weight, and yolk index. Additionally, the spray significantly reduced microbial load, including total plate count and E. coli. Eggs stored at 28 °C remained safe for 24–30 days, while those at 2–8 °C lasted over 42 days. This innovative film-forming spray offers a promising approach for preserving internal and external egg quality during storage. Full article

Fresh fruit are highly perishable commodities, facing significant postharvest losses primarily due to physiological deterioration and microbial spoilage. Conventional preservation methods often face limitations regarding safety, residue, and environmental impact. Because of its rapid decomposition and low-residue-impact characteristics, ozone has proven superior as an efficient and eco-friendly solution for preserving fruit quality after harvest. The maturation and aging processes of kiwifruit are closely linked to the involvement of reactive oxygen species (ROS) metabolism. This study aimed to investigate the effects of intermittent ozone treatment (21.4 mg/m³, applied for 0, 1, 3, or 5 h weekly) on ROS metabolism, the antioxidant defense system, and storage quality of kiwifruit during cold storage (0.0 ± 0.5 °C). The results showed ozone treatment slowed the decline in titratable acid (TA) content and fruit firmness, inhibited increases in total soluble solids (TSSs) and weight loss, and maintained the storage quality. Additionally, ozone treatment enhanced the activities of antioxidant-related enzymes. This includes superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX). Furthermore, it delayed the reduction in ascorbate (ASA), glutathione (GSH), total phenolic compounds, and flavonoid content, while also preventing the accumulation of ROS and the rise in malondialdehyde (MDA) levels. In summary, the results indicate that ozone treatment enhances the antioxidant capacity of kiwifruit by increasing the structural integrity of cell membranes, preserving the structural integrity of cell membranes, and effectively maintaining the storage quality of the fruit. Full article

Jujubes have a beautiful taste, and high nutritional and economic value. The planting area of dwarf and densely planted jujubes is large and shows an increasing trend; however, the mechanization level and efficiency of fresh jujube harvesting are low. For this reason, our research group conducted a study on mechanical harvesting technology for fresh jujubes. A pneumatic jujube harvester was designed. This harvester is composed of a self-regulating picking mechanism, a telescopic conveying pipe, a negative pressure generator, a cleaning mechanism, a double-chamber collection box, a single-door shell, a control assembly, a generator, a towing mobile chassis, etc. During the harvest, the fresh jujubes on the branches are picked under the combined effect of the flexible squeezing of the picking roller and the suction force of the negative pressure air flow. They then enter the cleaning mechanism through the telescopic conveying pipe. Under the combined effect of the upper and lower baffles of the cleaning mechanism and the negative-pressure air flow, the fresh jujubes are separated from impurities such as jujube leaves and branches. The clean fresh jujubes fall into the collection box. We considered the damage rate of fresh jujubes, impurity rate, leakage rate, and harvesting efficiency as the indexes, and the negative-pressure suction wind speed, picking roller rotational speed, and the inclination angle of the upper and lower baffles of the cleaning and selection machinery as the test factors, and carried out the harvesting test of fresh jujubes. The test results show that when the negative-pressure suction wind speed was 25 m/s, the picking roller rotational speed was 31 r/min, and the inclination angles of the upper and lower baffle plates for cleaning and selecting were −19° and 19.5°, respectively, the breakage rate of fresh jujube harvesting was 0.90%, the rate of impurity was 1.54%, the rate of leakage was 2.59%, and the efficiency of harvesting was 73.37 kg/h, realizing the high-efficiency and low-loss harvesting of fresh jujubes. This study provides a reference for the research and development of fresh jujube mechanical harvesting technology and equipment. Full article

This study aimed to investigate the efficacy of a composite coating composed of 150 mg/L ε-Poly-L-lysine (ε-PL) and 5 g/L chitosan (CTS) in extending the shelf life and maintaining the postharvest quality of fresh Tremella fuciformis. Freshly harvested T. fuciformis were treated by surface spraying, with distilled water serving as the control. The effects of the coating on storage quality, physicochemical properties, reactive oxygen species (ROS) metabolism, and membrane lipid metabolism were evaluated during storage at (25 ± 1) °C. The results showed that the ε-PL/CTS composite coating significantly retarded quality deterioration, as evidenced by reduced weight loss, maintained whiteness and color, and higher retention of soluble sugars, soluble solids, and soluble proteins. The coating also effectively limited water migration and loss. Mechanistically, the coated T. fuciformis exhibited enhanced antioxidant capacity, characterized by increased superoxide anion (O₂⁻) resistance capacity, higher activities of antioxidant enzymes (SOD, CAT, APX), and elevated levels of non-enzymatic antioxidants (AsA, GSH). This led to a significant reduction in malondialdehyde (MDA) accumulation, alongside improved DPPH radical scavenging activity and reducing power. Furthermore, the ε-PL/CTS coating preserved cell membrane integrity by inhibiting the activities of lipid-degrading enzymes (lipase, LOX, PLD), maintaining higher levels of key phospholipids (phosphatidylinositol and phosphatidylcholine), delaying phosphatidic acid accumulation, and consequently reducing cell membrane permeability. In conclusion, the ε-PL/CTS composite coating effectively extends the shelf life and maintains the quality of postharvest T. fuciformis by modulating ROS metabolism and preserving membrane lipid homeostasis. This study provides a theoretical basis and a practical approach for the quality control of fresh T. fuciformis. Full article

Lychee (Lychee chinensis Sonn.) is a tropical fruit highly appreciated for its vivid red color, sweet flavor, and nutritional properties. However, it is highly perishable, with postharvest losses often due to oxidative browning and dehydration. This study evaluated the organic olive oil coating (OC), a natural lipidic system with the potential to act as a precursor for organogel development, combined with polyamide/polyethylene (PA/PE) packaging under passive modified atmosphere. Fruits were harvested at commercial maturity and divided into two groups: OC-treated and untreated control (CTR). Both groups were stored at 5 ± 1 °C and 90 ± 5% relative humidity and analyzed on days 0, 3, 6, and 9. The OC-treated fruits showed significantly better retention of physical, chemical, microbiological, and sensory qualities. The coating reduced oxidative stress and enzymatic browning, preserving color and firmness. The PA/PE packaging regulated gas exchange, lowering oxygen levels and delaying respiration and ripening. As a result, OC fruits had lower weight loss, a slower increase in browning index and maturity index, and better visual and sensory scores than the CTR group. This dual strategy proved effective in extending shelf life while maintaining the fruit’s appearance, flavor, and nutritional value. It represents a sustainable and natural approach to enhancing the postharvest stability of lychee. Full article

In this study, we aimed to address the lack of systematic research on key collision dynamics parameters (elastic restitution coefficient) in the full mechanization of rapeseed operations, which hinders the development of precision agriculture. In this present work, the restitution coefficient of rapeseed was systematically investigated, and a predictive model (R² = 0.959) was also established by using Box–Behnken design response surface methodology (BBD-RSM). The results show that the collision restitution coefficient varies in the range of 0.539–0.649, with the key influencing factors ranked as follows: moisture content (M_c) > material layer thickness (L) > drop height (H). The EDEM simulation methodology was adopted to validate the experimental results, and the results show that there is a minimal relative error (−1% < δ < 1%) between the measured and simulated rebound heights, indicating that the established model shows a reliable prediction performance. Moreover, by comprehensively analyzing stress, strain, and energy during the collision process between rapeseed and Q235 steel, it can be concluded that the process can be divided into five stages—free fall, collision compression, collision recovery, rebound oscillation, and rebound stabilization. The maximum stress (1.19 × 10⁻² MPa) and strain (6.43 × 10⁻⁶ mm) were observed at the beginning of the collision recovery stage, which can provide some theoretical and practical basis for optimizing and designing rapeseed machines, thus achieving the goals of precise control, harvest loss reduction, and increased yields. Full article

Reducing post-harvest losses of cereal crops is a key challenge for ensuring global food security amid the limited arable land and growing population. This study investigates the effectiveness of electron beam irradiation (5 MeV, ILU-10 accelerator) as a physical decontamination method for various cereal crops cultivated in Kazakhstan. Samples were irradiated at doses ranging from 1 to 5 kGy, and microbiological indicators—including Quantity of Mesophilic Aerobic and Facultative Anaerobic Microorganisms (QMAFAnM), yeasts, and molds—were quantified according to national standards. Experimental results demonstrated an exponential decline in microbial contamination, with a >99% reduction achieved at doses of 4–5 kGy. The modeled inactivation kinetics showed strong agreement with the experimental data: R² = 0.995 for QMAFAnM and R² = 0.948 for mold, confirming the reliability of the exponential decay models. Additionally, key quality parameters—including protein content, moisture, and gluten—were evaluated post-irradiation. The results showed that protein levels remained largely stable across all doses, while slight but statistically insignificant fluctuations were observed in moisture and gluten contents. Principal component analysis and scatterplot matrix visualization confirmed clustering patterns related to radiation dose and crop type. The findings substantiate the feasibility of electron beam treatment as a scalable and safe technology for improving the microbiological quality and storage stability of cereal crops. Full article

Silos are strategic structures widespread in the industrial sectors for post-harvest preservation purposes. Current standards on the seismic design of silos are understandably based on approximate and simplified assumptions, leading intentionally to conservative design-oriented formulae. However, unjustified over-estimation might lead to unnecessary economic losses. As part of the authors’ analytical and experimental ongoing research on the complex seismic behavior of filled silo systems, in this short paper, an in-depth reading of the theoretical framework originally proposed during the 1970s and 1980s is provided to present a better understanding of the unexplained design-oriented formula of the seismic additional pressure in the European standard. A conceptual incongruence in the Eurocode EN1998-4:2006 is pointed out and discussed regarding the dynamic overpressure formula in the case of ground-supported flat-bottom circular silos subjected to seismic excitation. Specifically, a potential miscounting of the geometrical aspect in circular silos, with respect to rectangular ones, leads to an inconsistent amplification of the additional pressures in the range 1.65–2, depending on the filling aspect ratio of the silo. This inconsistency provides the reason for several unexplained results recently published in the scientific literature. A proposal for a physically based correction, retaining the current assumptions made by the EN1998-4, is finally given. Full article

Background and Objectives: In recent years, there has been growing interest in the production of ingredients rich in dietary fiber and antioxidants, such as green banana flours. This study evaluated the effect of consumption of mixed green banana pulp (PF) and peel (PeF) flours on the body weight gain, adiposity, lipid profile, and intestinal morphology of Wistar rats. Methods: Male young rats were divided into four groups (n = 8) that received a standard diet (SD), or one of the following three test diets: M1 (SD + 90% PF/10% PeF), M2 (SD + 80% PF/20% PeF), or P (SD + 100% PF) for 28 days. Results: Rats from M1, M2, and P groups showed reduced body weight gain and adiposity and had lower contents of total cholesterol, LDL-c, VLDL-c, and triglycerides. Animals from M1 and M2 groups had an increase in cecum weight, fecal moisture, acetic acid concentration, and crypt depth and reduced fecal pH. Moreover, consumption of the M1, M2, and P diets increased the expression of proteins involved in intestinal functionality. Significant negative correlations were observed between consumption of resistant starch and soluble dietary fiber, from the flours, and weight gain (r = −0.538 and r = −0.538, respectively), body adiposity (r = −0.780 and r = −0.767, respectively), total cholesterol (r = −0.789 and r = −0.800, respectively), and triglycerides (r = −0.790 and r = −0.786, respectively). Conclusions: Mixed green banana pulp and peel flour proved to be a viable alternative as a food ingredient that can promote weight loss, improve lipid profile and intestinal morphology, and minimize post-harvest losses. Full article

This paper presents a novel approach employing localized annealing through Joule heating to enhance the performance of Thin-Film Piezoelectric-on-Silicon (TPoS) MEMS resonators that are crucial for applications in sensing, energy harvesting, frequency filtering, and timing control. Despite recent advancements, piezoelectric MEMS resonators still suffer from anchor-related energy losses and limited quality factors (Qs), posing significant challenges for high-performance applications. This study investigates interface modification to boost the quality factor (Q) and reduce the motional resistance, thus improving the electromechanical coupling coefficient and reducing insertion loss. To balance the trade-off between device miniaturization and performance, this work uniquely applies DC current-induced localized annealing to TPoS MEMS resonators, facilitating metal diffusion at the interface. This process results in the formation of platinum silicide, modifying the resonator’s stiffness and density, consequently enhancing the acoustic velocity and mitigating the side-supporting anchor-related energy dissipations. Experimental results demonstrate a Q-factor enhancement of over 300% (from 916 to 3632) and a reduction in insertion loss by more than 14 dB, underscoring the efficacy of this method for reducing anchor-related dissipations due to the highest annealing temperature at the anchors. The findings not only confirm the feasibility of Joule heating for interface modifications in MEMS resonators but also set a foundation for advancements of this post-fabrication thermal treatment technology. Full article

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article