Search Results (539)

The scarcity of labeled hyperspectral video samples has motivated existing methods to leverage RGB-pretrained networks; however, many existing methods of hyperspectral object tracking (HOT) select only three representative spectral bands from hyperspectral images, leading to spectral information loss and weakened target discrimination. To address this issue, we propose the Band and Context Refinement Network (BCR-Net) for HOT. Firstly, we design a band importance learning module to partition hyperspectral images into multiple false-color images for pre-trained backbone network. Specifically, each hyperspectral band is expressed as a non-negative linear combination of other bands to form a correlation matrix. This correlation matrix is used to guide an importance ranking of the bands, enabling the grouping of bands into false-color images that supply informative spectral features for the multi-branch tracking framework. Furthermore, to exploit spectral–spatial relationships and contextual information, we design a Contextual Feature Refinement Module, which integrates multi-scale fusion and context-aware optimization to improve feature discrimination. Finally, to adaptively fuse multi-branch features according to band importance, we employ a correlation matrix-guided fusion strategy. Extensive experiments on two public hyperspectral video datasets show that BCR-Net achieves competitive performance compared with existing classical tracking methods. Full article

Tomato is a globally significant horticultural crop with substantial economic and nutritional value. High-precision phenotypic analysis of tomato fruit characteristics, enabled by computer vision and image-based phenotyping technologies, is essential for varietal selection and automated quality evaluation. An intelligent detection framework for phenomics analysis of tomato fruits was developed in this study, which combines image processing techniques with deep learning algorithms to automate the extraction and quantitative analysis of 12 phenotypic traits, including fruit morphology, structure, color and so on. First, a dataset of tomato fruit section images was developed using a depth camera. Second, the SegFormer model was improved by incorporating the MLLA linear attention mechanism, and a lightweight SegFormer-MLLA model for tomato fruit phenotype segmentation was proposed. Accurate segmentation of tomato fruit stem scars and locular structures was achieved, with significantly reduced computational cost by the proposed model. Finally, a Hybrid Depth Regression Model was designed to optimize the estimation of optimal depth. By fusing RGB and depth information, the framework enabled efficient detection of key phenotypic traits, including fruit longitudinal diameter, transverse diameter, mesocarp thickness, and depth and width of stem scar. Experimental results demonstrated a high correlation between the phenotypic parameters detected by the proposed model and the manually measured values, effectively validating the accuracy and feasibility of the model. Hence, we developed an equipment automatically phenotyping tomato fruits and the corresponding software system, providing reliable data support for precision tomato breeding and intelligent cultivation, as well as a reference methodology for phenotyping other fruit crops. Full article

Objectives: This study investigated the temporal dynamics of category-based attentional orienting (CAO) under the influences of prediction (top-down) and perceptual load (bottom-up) across color and shape dimensions, combining behavioral and event-related potential (ERP) measures. Methods: Across two experiments, we manipulated predictive validity and perceptual load during a visual search for category-defined targets. Results: The results revealed a critical dimension-specific effect of prediction: invalid predictions elicited a larger N2pc component (indexing attentional selection) for shape-defined targets, but not color-defined targets, indicating that shape CAO relies more heavily on predictive information during early processing. At the behavioral level, a combined analysis of the two experiments revealed an interaction between prediction and perceptual load on accuracy, suggesting their integration can occur at later stages. Conclusions: These findings demonstrate that prediction and perceptual load exhibit distinct temporal profiles, primarily independently modulating early attentional orienting, with their interactive effects on behavior being more nuanced and dimension-dependent. This study elucidates the distinct temporal and dimensional mechanisms through which top-down and bottom-up sources of uncertainty shape attentional orienting to categories. Full article

This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency–spatial enhancement and feature extraction, ETomS for efficient context-aware encoding, and SFAConv for selective multi-scale downsampling) to address subtle inter-stage color transitions, small fruit instances, and cluttered canopies. We benchmark ToRLNet against lightweight and small-scale YOLO baselines (YOLOv8–YOLOv12) and conduct controlled ablations isolating each module’s contribution. ToRLNet attains Precision 90.27%, Recall 86.77%, F1-score 88.49%, mAP50 91.76%, and mAP 78.01% with only 6.9 GFLOPs, outperforming representative nano/small YOLO variants under comparable compute budgets. Ablation results show WaveFusionNet improves spectral–textural robustness, ETomS balances the precision–recall trade-off while reducing redundancy, and SFAConv preserves fine chromatic gradients and boundary structure during downsampling; their combination yields the most balanced performance. These findings demonstrate that ToRLNet delivers a favorable accuracy–efficiency trade-off and provides a practical foundation for on-board perception in automated harvesting, yield estimation, and greenhouse management. Full article

Tea Anthracnose (TA), caused by fungi of the genus Colletotrichum, is one of the major threats to global tea production. UAV remote sensing has been explored for non-destructive and high-efficiency monitoring of diseases in tea plantations. However, variations in illumination, background, and meteorological factors undermine the stability of cross-temporal data. Data processing and modeling complexity further limits model generalizability and practical application. This study introduced a cross-temporal, generalizable disease monitoring approach based on UAV multimodal data coupled with relative-difference standardization. In an experimental tea garden, we collected multispectral, thermal infrared, and RGB images and extracted four classes of features: spectral (Sp), thermal (Th), texture (Te), and color (Co). The Normalized Difference Vegetation Index (NDVI) was used to identify reference areas and standardize features, which significantly reduced the relative differences in cross-temporal features. Additionally, we developed a vegetation–soil relative temperature (VSRT) index, which exhibits higher temporal-phase consistency than the conventional normalized relative canopy temperature (NRCT). A multimodal optimal feature set was constructed through sensitivity analysis based on the four feature categories. For different modality combinations (single and fused), three machine learning algorithms, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP), were selected to evaluate disease classification performance due to their low computational burden and ease of deployment. Results indicate that the “Sp + Th” combination achieved the highest accuracy (95.51%), with KNN (95.51%) outperforming SVM (94.23%) and MLP (92.95%). Moreover, under the optimal feature combination and KNN algorithm, the model achieved high generalizability (86.41%) on independent temporal data. This study demonstrates that fusing spectral and thermal features with temporal standardization, combined with the simple and effective KNN algorithm, achieves accurate and robust tea anthracnose monitoring, providing a practical solution for efficient and generalizable disease management in tea plantations. Full article

This study aims to improve the accuracy and efficiency of apple defect detection under complex lighting conditions. A novel approach is proposed that integrates filtered imaging with color space reconstruction, utilizing YOLOv12 as the detection framework. “Red Fuji” apples were selected, and an imaging platform featuring adjustable illumination and RGB filters was established. Following pre-experimental optimization of imaging conditions, a dataset comprising 1600 images was constructed. Conversions to RGB, HSI, and LAB color spaces were performed, and YOLOv12 served as the baseline model for ablation experiments. Detection performance was assessed using Precision, Recall, mAP, and FPS metrics. Results indicate that the green filter under 4500 K illumination combined with RGB color space conversion yields optimal performance, achieving an mAP50–95 of 83.1% and a processing speed of 15.15 FPS. This study highlights the impact of filter–color space combinations on detection outcomes, offering an effective solution for apple defect identification and serving as a reference for industrial inspection applications. Full article

Rolling is a critical step in the processing of black tea, marking the beginning of fermentation. At this stage, the formation of tea pigments causes significant changes in the color of the processed leaves, laying the essential groundwork for the development of color and flavor quality components in subsequent fermentation processes. However, the rapid and non-destructive sensing of tea pigments during black tea rolling remains challenging. This study focused on black tea products undergoing rolling as its research subject, utilizing electrical characteristic detection technology to collect time-series electrical parameters of rolling leaves at various testing frequencies. The original electrical parameters were preprocessed using multiplicative scatter correction (MSC), min-max normalization (Min-Max), and smoothing (Smooth). Various selection methods, including the competitive adaptive reweighting algorithm (CARS), uninformative variable elimination (UVE), and the variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV), were employed to identify electrical parameters relevant to the targeted attributes. Quantitative prediction models for the content of tea pigments were established using partial least squares regression (PLSR) and support vector machine regression (SVR). The results demonstrated that the Smooth-VCPA-IRIV-SVR model exhibited superior performance in predicting the contents of theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs). Correlation coefficients of prediction (R_p) all exceeded 0.99, and Relative prediction deviation (RPD) values were all above 6.5, indicating that the model enables rapid and non-destructive detection of tea pigment content during black tea rolling. These findings provide preliminary technical support and reference for the digital production of black tea. Full article

To rationally utilize Morchella germplasm resources, this study investigated 13 phenotypic traits in 231 Chinese Morchella germplasm accessions. Accessions were stratified by cap color and subjected to comparative analyses using four sampling methods, five sampling intensities, two genetic distance metrics, and four hierarchical clustering algorithms to determine the optimal strategy for core collection construction. The optimal sampling strategy for core collection construction was identified using six evaluation. Phenotypic traits of the core collection were evaluated using genetic diversity eigenvalues, t-tests, F-tests, and systematic clustering, with confirmation via principal component analysis. The results indicate that the logarithmic ratio method yielded the smallest differences in group proportions, making it the optimal sampling method. A 15% sample intensity proved optimal, with Euclidean distance outperforming Mahalanobis distance. The longest-distance method was determined to be the optimal clustering approach. Within the optimal sampling strategy combination, the CR value reached its maximum (97.77%). Ultimately, 34 Morchella germplasm resources were extracted, accounting for 14.72% of the total germplasm (original germplasm). The mean values, standard deviations, and genetic diversity of phenotypic traits were similar between the original germplasm and the core collection. However, the coefficient of variation for quantitative traits showed significant differences. In the t-test, only the maturity period showed a significant difference. In the F-test, only the cap length/width and maturity period showed significant differences. Cluster analysis grouped the germplasm resources of the core collection and the original germplasm into relatively consistent clusters. In principal component analysis, the eigenvalues and cumulative contribution rates of the first four principal components were higher for the core collection than for the original germplasm. This indicates that the core collection eliminated most genetic redundancy while preserving the genetic diversity of the original germplasm. The core collection selection is representative and can be effectively utilized as breeding material. This study provides a reference for the effective utilization and germplasm innovation of Morchella germplasm resources. Full article

This exploratory study assessed the influence of botanical origin and acquisition source on the phenolic profile and antioxidant properties of selected Romanian monofloral honeys. Eight samples were analyzed, representing five floral types: acacia, linden, rapeseed, lavender, and thyme. For acacia, linden, and rapeseed, both commercial and locally sourced honeys were included. Analytical techniques included total phenolic content (TPC, Folin–Ciocalteu), antioxidant assays (DPPH, ABTS, FRAP), color intensity (ABS₄₅₀), and phenolic compound profiling via HPLC-DAD-ESI⁺. TPC ranged from 179.26 ± 23.57 to 586.67 ± 18.33 mg GAE/100 g, with thyme and linden honeys presenting the highest values. Seventeen phenolic compounds were tentatively identified; gallic acid was predominant in thyme honey (127 mg/100 g), and linden honey contained high levels of rutin (70 mg/100 g) and galangin-glucoside. Antioxidant capacity varied notably by floral origin, with thyme and linden outperforming acacia samples. Significant correlations were found between total phenolics and ABTS (r = 0.86), and between ABS₄₅₀ and FRAP (r = 0.86). DPPH kinetics followed zero-order behavior (R² > 0.98). Principal component analysis (PC1 + PC2 = 88%) enabled preliminary separation by botanical origin. While based on a limited sample set, findings support the relevance of combining chromatographic, kinetic, and multivariate tools for exploratory honey characterization. Full article

Soil organic matter (SOM) is a key component of nutrient cycling and soil fertility in terrestrial ecosystems. SOM is of great significance to the stability of terrestrial ecosystems and the improvement of soil productivity; to further exert its role, it is first necessary to clarify its actual distribution and occurrence status in specific regions. Under the combined impacts of intensive agriculture, unreasonable farming practices, and climate change, the SOM content in the Songnen Plain is showing a degradation trend, posing multiple stresses on its soil ecosystem functions. This study aims to systematically track the dynamic changes of SOM in the Songnen Plain, assess its spatiotemporal evolution characteristics, and reveal its driving mechanisms. A total of 113 representative soil profiles were selected in 2023; standardized excavation and sampling procedures were employed in the Songnen Plain. Soil pH, SOM, total nitrogen (TN), total phosphorus (TP), total potassium (TK), particle size (PSD), texture, and Munsell soil colors of samples were determined. Temporal variation characteristics, as well as horizontal and vertical spatial distribution patterns, in SOM content in the Songnen Plain were assayed. Structural equation modeling (SEM), together with freeze–thaw of soil and soil color mechanism analyses, was applied to reveal the spatiotemporal dynamics and driving mechanisms of SOM. The result indicated that the distribution pattern of SOM content in horizontal space shows higher levels in the northeastern region and lower levels in the southwestern region, and decreased with increasing soil depth. SEM analysis indicated that TN and PSD were the main positive factors, whereas bulk density exerted a dominant negative effect. The ranking of contribution rates is TN > TK > TP > PSD > annual average temperature > annual precipitation > bulk density. Mechanistic analysis revealed a significant negative correlation between SOM content and R, G, B values, with soil color intensity serving as a visual indicator of SOM content. Freeze–thaw thickness of soil was positively correlated with SOM content. These findings provide a scientific basis for soil fertility management and ecological conservation in cold regions. Full article

Color accuracy is both important and elusive in cultural heritage imaging. An established method for improving color accuracy is dual-RGB imaging, where RGB images of an object are captured sequentially under two different conditions and then combined. As part of an initiative to increase accessibility to color-accurate imaging, the use of lighting filters with the dual-RGB method is investigated. Gel lighting filters are low-cost and can be directly integrated into an imaging workflow by placing them in front of the existing light sources. This research found that color accuracy can be increased by using lighting filters, but it can also be decreased by a poor selection of filter combinations. The identity of the best-performing filters is highly dependent on the light source and can be affected by the pixels selected to represent the color target. Current simulation approaches are insufficient to predict which filters will increase color accuracy. While lighting filters are a promising method for accessible multispectral imaging, their practical implementation is complex and requires further research and adjustments to the method. Full article

The skin color of radish taproots is an important commodity character that directly affects the choice behavior of consumers. Here, we identified a skin color gene carried by a red-skinned inbred line, SXAU-R2. Genetic population was constructed by the crossing of SXAU-R2 and a white-skinned inbred line, SXAU-W2, and the taproots of F₁ plants exhibited intermediate color. In the F₂ population, the separation ratio of taproot skin color indicated that the phenotype was controlled by one major locus, named RST1 (Red-Skinned Taproot 1). Combined with bulked segregant analysis and RNA sequencing (BSA-seq), 2640 single nucleotide polymorphisms (SNPs) were detected between the annotated genes of the red skin bulk and white skin bulk. Molecular markers were developed in the SNP-enriched 27~32 Mbp region of chromosome 7, and then RST1 was mapped in the genetic interval between flanking markers SSR-14 and SSR-22. Using F_2:3 lines derived from a key F₂ heterozygote, RST1 was narrowed down into a 530 Kbp interval. There were 46 expressed annotated genes in the fine-mapping region, and a gene encoding MYB was selected as the candidate of RST1. Finally, based on Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and RT-qPCR, we identified the potential interacting genes RsbHLH and RsWD, as well as the latent target genes RsDFR and RsANS of RST1 in the anthocyanin synthesis pathway. These results provide an understanding of the genetic mechanisms regulating anthocyanin synthesis and offer an efficient molecular marker for the radish breeding of skin color. Full article

Dioscorea polystachya (Chinese yam) is a crop valued for both medicinal and edible purposes, and exhibits rich genetic diversity. However, research into its germplasm resources remains understudied, and molecular breeding efforts lag behind. To bridge this gap, this study employed an integrated approach, combining the analysis of 23 phenotypic traits (17 qualitative and 6 quantitative) with genotyping using 19 polymorphic SSR markers. This combined strategy was applied to 53 accessions collected across 16 Chinese provinces to assess genetic diversity, population structure, and marker–trait associations. Phenotypic analysis revealed high diversity, with the Shannon diversity index (I) ranging from 0.09 to 1.15 for qualitative traits and from 1.45 to 1.79 for quantitative traits. Tuber traits exhibited the highest variability (with a CV up to 71.45%), indicating significant potential for yield improvement. Principal component analysis distilled phenotypic variation into eight principal components (accounting for 73.13% of the cumulative variance), and elite germplasm (e.g., DP24, DP52) was selected for breeding based on this analysis. Stepwise regression prioritized eight core evaluation traits (e.g., flowering rate, tuber length). SSR markers amplified 80 alleles (mean 4.211/locus), showing moderate genetic diversity (He = 0.529, PIC = 0.585). Population structure analysis divided accessions into two subpopulations, correlated with geographic origins: Group 1 (northern/southwestern China) and Group 2 (central/eastern China), reflecting adaptation to local climates and human selection. Association analysis identified 10 SSR loci significantly linked (p < 0.01) to key traits, including YM07_2 (flowering, R² = 13.94%), YM37_2 (leaf margin color, R² = 19.03%), and YM19_3 (leaf width, R² = 19.34%). This study establishes a comprehensive genetic framework for Chinese yam, offering molecular tools for marker-assisted breeding and strategies to conserve high-diversity germplasm, thereby enhancing the utilization of this orphan crop. Full article

In response to the actual needs of pure copper bonding wires, it is crucial to develop a chromium-free passivator that is environmentally friendly and has excellent corrosion resistance. In this study, three different composite organic formulations of chromium-free passivation solutions are selected: 2-Amino-5-mercapto-1,3,4 thiadiazole (AMT) + 1-phenyl-5-mercapto tetrazolium (PMTA), 2-mercaptobenzimidazole (MBI) + PMTA, and Hexadecanethiol (CHS) + sodium dodecyl sulfate (SDS). The performance analysis and corrosion mechanism were compared with traditional hexavalent chromium passivation through characterization techniques such as XRD, SEM, and XPS. The results show that the best corrosion resistance formula is the combination of the PMTA and MBI passivation agent, and all its performances are superior to those of hexavalent chromium. The samples treated with this passivation agent corrode within 18 s in the nitric acid drop test, which is better than the 16 s for Cr⁶⁺ passivation. The samples do not change color after being immersed in salt water for 48 h. Electrochemical tests and high-temperature oxidation test also indicate better corrosion resistance than Cr⁶⁺ passivation. Through the analysis of functional groups and bonding, the excellent passivation effect is demonstrated to be achieved by the synergistic action of the chemical adsorption film formation of PMTA and the anchoring effect of MBI. Eventually, a dense Cu-PMTA-BMI film is formed on the surface, which effectively blocks the erosion of the corrosive medium and significantly improves the corrosion resistance. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article