Search Results (3,585)

The triboelectric nanogenerator (TENG), which converts mechanical energy into electrical energy through the coupled effect of triboelectrification and electrostatic induction, has garnered significant interest among researchers due to its portability and self-powered characteristics. Despite its evident development potential, TENG continues to face challenges, including the necessity to enhance its triboelectric performance through the optimization of structures, materials, and manufacturing techniques to improve energy conversion efficiency. Additionally, its environmental stability and durability also need to be improved. TENGs designed inspired by non-human animals and plants offer feasible solutions to address these limitations. These bio-inspired TENGs optimize the structural design of TENGs and the materials of the triboelectric layers by imitating the structures, functions, and behaviors of organisms, thereby further improving the energy conversion efficiency, sensitivity, wear resistance, adaptability to special environments, biocompatibility, and wearing comfort of TENGs. This paper expounds on the progress of TENGs inspired by non-human animals and plants applied in environmental energy harvesting, human health and motion monitoring. It also discusses the current challenges, with a view to providing insights for the interdisciplinary integration and development of bionics and TENGs. Full article

Melatonin is a small-molecule indoleamine compound that is essential to life. It is widely present in plants and plays an extremely important role in plant growth and fruit quality. This article summarizes the role of melatonin synthesis in plants, its distribution in some fruits, and its impact on post-harvest fruit quality. The mechanisms by which melatonin regulates post-harvest fruit quality are discussed in depth from the perspectives of respiratory metabolism, ethylene metabolism, sugar metabolism, reactive oxygen species (ROS), and antioxidant systems. Finally, we analyze the existing problems in the current research field of melatonin in post-harvest fruit preservation and look toward the application prospects of melatonin synthesis, metabolism, and the mechanisms for regulating fruit quality. This article provides a reference for further exploring the role of melatonin and increasing the endogenous content of melatonin in fruits, as well as post-harvest preservation techniques, with the aim of promoting the application of melatonin in post-harvest preservation. Full article

Rice is one of the world’s most important food crops, feeding over half of the global population and being crucial for food security. Accurate, timely mapping of rice fields is essential for precision agriculture, yet conventional methods relying on static samples fail to capture dynamic farmers’ planting decisions. To address this, we propose the Multi-Source Dynamic Sample Generation and Phenology-Guided Feature Selection Framework for In-Season Rice Identification (MSDF-RiceID) using multi-source remote sensing imagery. It incorporates two key innovations: (i) a rule-based sample updating mechanism based on historical rice maps and a dynamic threshold algorithm, and (ii) phenology-guided feature optimization through exponential weighting. Developed specifically to handle complex cropping patterns and high cloud cover in Hunan Province, MSDF-RiceID integrates these innovations within a grid-search-optimized Random Forest classifier to produce reliable monthly rice distribution maps. In-season samples corresponding to transplanting dates in April (DOY 100, 120), June (DOY 160), and July (DOY 184), differentiated as early-, middle-, and late-rice crops. The optimal feature set combined Sentinel-1 (PRI, VH, VH_VV), Sentinel-2 (NDYI, PSRI, NDBI, NDWI), and MODIS (NDVI, EVI, NDBI, LSWI) indices. Accuracy increased seasonally, with F1-score rising from 0.82 in May to 0.97 at harvest. Cross-region validation in Taishan (Guangdong) and Panjin (Liaoning) showed that the earliest identifiable stage (F1-score > 0.9) occurred earlier than in Hunan due to Hunan’s more complex triple-cropping phenology, highlighting the model’s strong transferability. Furthermore, MSDF-RiceID outperformed existing products (TWDTW-Rice and EARice10), increasing overall accuracy by 0.12–0.18, Kappa by 0.23–0.35, and F1-score by 0.09–0.15. These results demonstrate its effectiveness for in-season, large-scale, and dynamic rice mapping under persistent cloud cover, thereby providing direct support for precision agricultural management in heterogeneous cropping systems. Full article

Mungbean is an important legume crop native to India. In this study, 500 indigenous mungbean accessions collected from diverse eco-geographical regions of India were evaluated for agronomic trait genetic variability and core collection development. The accessions were grown in an augmented design during 2019 and 2020, and data were recorded for seven quantitative and 13 qualitative traits. Analysis of variance (ANOVA), frequency distribution, and box-plot analyses revealed substantial phenotypic variation among the accessions. Traits including plant height (PHT), number of pods per plant (NPP), hundred-seed weight (HSW), and single-plant yield (SPY) exhibited high heritability coupled with high genetic advance, indicating the predominance of additive genetic effects. Principal component analysis showed that the first three principal components explained 70% of the total phenotypic variation. The Shannon–Weaver diversity index further indicated high levels of genetic diversity within the population. Based on quantitative traits, the accessions were grouped into six major clusters and 42 sub-clusters, with SPY, NPP, HSW, PHT, and days to 50% flowering (DFF) contributing substantially to genetic divergence. Correlation analysis suggested that direct selection for SPY and indirect selection through associated traits, including NPP, HSW, PHT, NSP, and pod length (POL), may enhance yield improvement. The germplasm collection also possessed desirable traits such as high yield potential, contrasting maturity groups, and plant types suitable for mechanical harvesting and bold-seeded type. A representative core set comprising 50 accessions was developed using the PowerCore program, providing valuable genetic resources for mungbean breeding and genetic improvement programs. Full article

This paper addresses the problem of efficient federated learning in energy-harvesting AIoT systems, where time-varying energy availability may lead to device blackouts and unstable learning performance. To address this issue, we propose an energy-adaptive multi-dimensional learning control framework that jointly determines model complexity and training intensity based on the real-time energy state of each device. This method integrates multiple control dimensions, including model pruning, quantization, knowledge distillation, and adaptive local training, into a unified decision mechanism under an energy constraint. Each device determines its participation in federated learning based on its residual energy relative to an energy threshold. When participating, the device selects a feasible learning configuration that jointly considers training intensity (e.g., epoch size and batch size) and lightweight learning operations to maximize learning effectiveness while preventing energy depletion. The proposed framework was implemented on a real-world testbed using NVIDIA Jetson Orin Nano devices under solar-energy-harvesting conditions. Our experimental results demonstrate that the proposed method significantly reduces device blackout while maintaining competitive model accuracy with respect to energy-unconstrained scenarios. These results highlight that joint control of multiple learning-cost factors is essential for achieving stable and efficient federated learning in energy-harvesting AIoT environments. Full article

Focusing on the self-powering demand of aircraft engine bladed disks (blisks), this paper investigates piezoelectric vibration energy-harvesting modeling and non-linear circuit performance. A multi-sector electromechanical coupled model is established to analyze the frequency splitting and vibration localization induced by minor structural mistuning. By breaking the cyclic symmetry, mistuning severely concentrates vibration energy into a specific sector, providing a localized high-energy concentration region for optimal energy extraction. To enhance recovery efficiency and load adaptability, three interface circuit topologies—Standard Energy-Harvesting (SEH), Parallel Synchronized Switch Harvesting on Inductor (P-SSHI), and Double Synchronized Switch Harvesting (D-SSHI)—are comparatively analyzed. Through wideband spatial–spectral dynamic response and steady-state impedance matching analyses, the non-linear energy conversion and transfer mechanisms are systematically characterized. Results demonstrate that synchronized switching circuits significantly improve energy transmission via forced voltage inversion, accompanied by a notable equivalent stiffness enhancement effect induced by electromechanical coupling. Furthermore, the D-SSHI topology not only exhibits substantial advantages in peak power extraction, but also, owing to its internal LC energy decoupling mechanism, forms a broad load-independent power plateau across an extremely wide impedance range. This research provides robust theoretical foundations for designing highly resilient self-powered intelligent blades under extreme operating conditions. Full article

Water conservation and grain yield improvement are primary objectives for sustainable agricultural development in arid and semi-arid regions. Variety selection, planting density, and irrigation management represent crucial agronomic practices that regulate root–crown growth and grain yield in maize. A two-year field experiment was carried out from 2021 to 2022 in Tongliao, Inner Mongolia Autonomous Region, China. Two widely cultivated maize varieties, DK159 and ZD958, were used as test materials. Two planting densities were designed: 60,000 plants ha⁻¹ (D1, local farmers’ conventional density) and 90,000 plants ha⁻¹ (D2). Five irrigation levels were established: 450 mm (I5, local farmers’ practice, CK), 360 mm (I4), 270 mm (I3), 180 mm (I2), and 90 mm (I1). We investigated the interactive effects of variety, planting density, and irrigation amount on dry matter accumulation pre- and post-silking, root spatial distribution characteristics, and the coordination mechanism of root–shoot growth in maize under shallow-buried drip irrigation. The results indicated that grain yield under DK159 was 5.37–6.69% higher than that under ZD958, and the yield under D2 was 13.32–15.89% higher than that under D1. At the D1 density, no significant difference in grain yield was observed between I2 and I5, with yields ranging from 12.90 to 13.92 t ha⁻¹. At the D2 density, grain yield under I3 was statistically similar to that under I5, ranging from 15.54 to 17.39 t ha⁻¹. Compared with local farmers’ conventional planting density and full irrigation regime, increasing planting density and reducing irrigation amount altered the vertical root distribution of maize. The proportion of roots distributed in the 0–20 cm topsoil layer decreased, while appropriate water deficit markedly increased root proportion in the 40–60 cm subsoil layer. Increasing planting density and moderately reducing irrigation effectively promoted pre- and post-silking dry matter accumulation while maintaining a high harvest index (HI). At silking stage, the root–shoot ratio increased initially and then stabilized with increasing irrigation amount. At maturity, the root–shoot ratio gradually decreased and tended to be stable as irrigation increased. Therefore, the adoption of water-efficient maize varieties, combined with appropriately increased planting density and optimized irrigation regimes, can coordinate root–shoot relationships in the early growth period, facilitate early root establishment and late-stage nutrient accumulation, and thus improve maize yield. Under the conditions of shallow-buried drip irrigation in the supplementary irrigation area of the West Liaohe Plain, the adoption of water-saving maize varieties, appropriately increased planting density, and optimized irrigation regimes can coordinate the developmental relationship between root and above-ground growth, promote early root development and late-stage nutrient accumulation, and thereby simultaneously increase maize grain yield. These results provide practical theoretical and technical references for achieving high-yield and water-saving maize production under similar ecological conditions. Full article

The yield, chemical profile and antioxidant activity of sage (Salvia officinalis L.) essential oils (SEOs) isolated from shaded (pearl-, red- and blue-colored nets) or non-shaded plants from three different harvest-time phenological stages (May, August and September) investigated. Both main effects and their interactions were highly significant (p < 0.01). Blue nets produced the highest yield in the first (4.09 mL/100 g) and second (3.29 mL/100 g) harvests, significantly exceeding all other treatments within the same harvest period. In the third harvest, unshaded control plants achieved the highest yield (3.55 mL/100 g). The total number of individual SEO components varied depending on the harvest time and shading treatment (27–35). The most abundant components were thujone (cis-thujone, 24.1–36.1%; trans-thujone, 4.9–13.1%) camphor (20.0–30.2%) and 1,8-Cineole (8–11%). The content of undesirable component camphor was the lowest in all three harvests in plants covered with blue shading nets. FRAP values ranged from 0.462 mg Fe²⁺/g oil (second harvest, red net) to 1.151 mg Fe²⁺/g oil (first harvest, red net), while EC₅₀ values ranged from 9.169 mg/mL (first harvest, red net) to 37.004 mg/mL (third harvest, blue net). The third-harvest blue-net sample exhibited one of the highest FRAP values (1.123 mg Fe²⁺/g oil) yet the weakest DPPH radical scavenging activity (EC₅₀ = 37.004 mg/mL), reflecting different mechanisms of antioxidant action between the two assays. In conclusion, the highest yield and best quality of EO were achieved in the first harvest. Shading the plants, particularly with blue nets, contributed to an increase in EO yield, as well as improved EO quality, including higher thujone content and lower content of undesirable camphor. Covering the plants with blue nets during the first and second harvest periods enhanced essential oil yield and quality. In the third harvest, open-field conditions favored a higher yield; however, blue-net shading produced the lowest camphor content (20.3%), which may be advantageous for pharmaceutical applications. The choice of whether to maintain or remove nets during the third harvest should therefore be guided by the intended end use of the essential oil. Full article

Plant height is a fundamental quantitative phenotypic trait affecting maize (Zea mays) planting density and is an important focus in the breeding of varieties suitable for mechanical harvesting. In this study, we found a natural extreme dwarf mutant designated as d25, whose dwarf phenotype is controlled by a single recessive gene. The phenotype was restored by spraying with gibberellic acid 3 (GA3), which indicated that the mutant phenotype of the d25 mutant resulted from mutations in genes involved in the gibberellin (GA) metabolic pathway. Additionally, performing bulked segregant analysis on 30 extreme phenotypic plants in the F2 population (d25 × P002), we located one major quantitative trait locus (QTL) at chromosome 3 from 9.3 to 11 Mb. In combination with transcriptome sequencing analysis of d25 and WT plants, we identified the cloned typical plant height-related gene D1, whose expression was significantly higher in d25 mutant plants than that in WT plants. Further analysis revealed that a 275 bp structural variant spanning exonic and intronic regions of the D1 gene accounted for the dwarf phenotype in the d25 mutant. Protein prediction revealed that this variant site alters the translated protein sequence of this region, thereby modifying its function and impairing the critical pathway responsible for hormone synthesis, resulting in reduced plant height. These findings indicate that the d25 mutant may be a novel mutant allele of the D1 gene affecting maize plant development. Full article

In response to the challenges of large fruit-scale variation, dense target distribution, severe leaf occlusion, and complex backgrounds in green plum detection within orchards, this paper proposes a lightweight multi-scale edge geometry-aware network (MEGNet). First, the Green Plum Detection Dataset (GPD) is constructed to provide realistic orchard scene data for the task. Next, we enhance the model’s structure based on YOLO11n by designing an efficient multi-scale feature fusion attention module (EMFFA) to improve the expression of multi-scale fruit features. We also introduce a color-edge guided dual-discriminator feature enhancement module (CED) to strengthen feature discrimination in complex backgrounds. A coordinate attention ghost detection head (CAGDetect) is proposed to reduce model parameters and computational complexity. Additionally, a geometry-consistency modulated CIoU loss function (GC-CIoU) is introduced to improve target localization stability in occluded and dense scenes by incorporating a geometric consistency modulation mechanism. Experimental results show that on the GPD, MEGNet achieves a Precision of 93.9%, Recall of 86.2%, mAP₅₀ of 93.2%, and mAP_50:95 of 76.1%. The model’s Parameters are only 2.13 M, with FLOPs of 4.7 G. Compared to the baseline YOLO11n model, Precision, Recall, mAP₅₀, and mAP_50:95 are improved by 2.5%, 5.2%, 4.4%, and 4.6%, respectively. Additionally, deployment experiments on the Jetson Orin Nano embedded device demonstrate real-time detection speeds of 31–33 FPS. The proposed method provides an efficient and reliable solution for intelligent harvesting systems, orchard monitoring platforms, and agricultural robot vision perception. Full article

Although bistable vibration energy harvesters offer promising broadband characteristics, their efficiency is often hindered by fixed potential barriers that confine the system to small-amplitude intra-well motion. The core innovation of this work is the proposal of a synchronous potential barrier regulation mechanism for multiple subsystems based on a rhombus linkage mechanism. This study introduces a novel multi-subsystem bistable vibration energy harvester (MBEH) integrated with a rhombus linkage mechanism to achieve tunable potential barriers. The mechanism facilitates the coupling of four bistable subsystems, where adjusting the magnet spacing of one subsystem allows for the synchronous regulation of magnetic gaps in others. This architecture ensures a continuous and precise optimization of the potential barrier. Consequently, this mechanism yields remarkable performance advancements, achieving highly efficient coupling among subsystems. Furthermore, potential barrier regulation efficiency is substantially increased, while operating bandwidths of subsystems are complementary and superimposed. Results from numerical investigations indicate that at an excitation acceleration of 0.6 g, MBEH outperforms conventional BEH with a 13.58 Hz increase in summed subsystem bandwidth and a 0.0223 μW gain in output power. The findings validate the efficacy of the proposed MBEH as a high-performance solution for robust broadband vibration energy harvesting. Full article

Plant height is a key agronomic trait that influences plant architecture and mechanical harvesting suitability in cotton; however, the molecular mechanisms underlying its dynamic development remain unclear. In this study, two recombinant inbred line (RIL) populations sharing CCRI127 as a common paternal parent (RIL-GH07, n = 150; RIL-2358B, n = 276) were developed. Based on stable plant-height performance across multiple environments, tall and short extreme lines were selected from the two RIL populations for transcriptome sequencing. By integrating differential expression analysis with weighted gene co-expression network analysis (WGCNA), we identified hub genes associated with cotton plant height development, characterized the molecular features and core pathways governing dynamic stem elongation at different growth stages, thereby providing insights into the transcriptional regulation of plant height development in cotton. The two RIL populations showed broadly similar plant-height growth patterns, with slow elongation at 15 DOS, rapid elongation during 30–60 DOS, and reduced growth after 70 DOS. Transcriptome differential expression analysis identified 15,052 non-redundant DEGs, which exhibited clear population- and stage-specific expression patterns. In the GH07 population, the largest number of DEGs was detected at 15 DOS (7193), whereas in the 2358B population relatively large numbers of DEGs were maintained at both 30 DOS (3839) and 70 DOS (3118). Analysis of DEGs shared by the two populations across four developmental stages showed that, in addition to genes with consistent expression trends, each stage also contained a substantial number of DEGs with opposite expression directions. WGCNA identified 25 gene expression modules, among which the green and yellow modules were significantly positively correlated with plant height. Functional enrichment analysis indicated that genes in these two modules were mainly enriched in hormone regulation and signal transduction, protein modification and degradation, and intracellular transport. Seven hub genes were identified by integrating intramodular connectivity and kME values. Functional prediction suggested that these genes may play important roles in cotton plant height development. This study provides genetic resources and a theoretical basis for subsequent functional validation of cotton plant height-related genes and the improvement of plant architecture in cotton. Full article

The carob tree (Ceratonia siliqua L.) is a typical tree of the arid Mediterranean, and its cultivation contributes to the sustainability of local agroecosystems. In recent years, the economic and environmental importance of the carob tree has grown due to its use as a raw material in the food, pharmaceutical, and cosmetic industries. It also plays an ecological role in conserving biodiversity and promoting sustainable agricultural systems by improving cultivation and mechanization strategies. Currently, national carob groves are facing competition from other more profitable crops such as olive, citrus, almond and horticultural systems. This has led to the marginalization of carob cultivation in several Mediterranean rural areas and increased the need to modernize and mechanize harvesting to enhance the potential of carob and its derived products. This study aimed to investigate the physical characteristics of the fruit (weight, length, width and fruit detachment force) in relation to the degree of ripeness, with the objective of providing useful information on the optimal harvesting period and introducing semi-mechanical harvesting systems. Full article

To address technical challenges such as equipment clogging and tuber damage during the mechanized harvesting of Xiangsha taro, this study designed a high-speed sliding-cutting device and conducted preliminary field performance evaluations. Based on the preliminary morphological baseline of Xiangsha taro and the distribution of soil penetration resistance, a multi-tooth rotary disc cutting device was developed. Kinematic and dynamic modelling indicated that a velocity ratio of 3.5–5.5 facilitate a ‘cycloidal loop’ trajectory, which theoretically reduces the potential for root disturbance by mitigating forward pushing forces. Initial field tests under specific orderly ridge conditions yielded a cutting qualification rate exceeding 96% and an estimated field capacity of 0.025 ha/h. While these results offer a preliminary technical reference for segmented harvesting equipment, the current validation is limited by the idealized row alignment of the experimental plot. Future research must evaluate the system’s adaptability to field irregularities and conduct direct controlled comparisons with commercial manual devices to fully substantiate its practical superiority. Full article

Accurate and rapid localization of tomato pedicel picking points presents a significant challenge for automated harvesting, due to factors such as occlusion by dense foliage, overlapping fruits, variable lighting conditions, and the slender morphology of pedicels. To address these, we propose an integrated picking decision system combining enhanced instance segmentation with RGB-D fusion. In this study, a lightweight detection model named YOLOv8n-EED-seg is introduced. An optimized EfficientRep backbone is integrated to enhance computational efficiency, while the EMAttention mechanism and a refined DynamicHead module strengthen multi-scale feature representation for slender pedicels. The model further incorporates the Zhang–Suen algorithm for skeleton extraction and a large-neighborhood mean method for depth restoration, enabling precise 3D localization. Experiments are conducted on a dataset of 3310 images collected in a greenhouse environment. Compared with the baseline YOLOv8n-seg, our model improves precision, recall, F1 score, and mAP₅₀ by 5.09%, 2.78%, 3.63%, and 4.31%, respectively. The system achieves an inference speed of 4.8 ms per frame, enabling real-time performance, while attaining a 93.88% success rate in 3D picking-point localization. Furthermore, the proposed model demonstrates superior robustness in complex environments compared with common segmentation models, effectively balancing accuracy, speed, and model complexity. This study provides a reliable technical pathway for high-precision, vision-based tomato-harvesting robots. Full article