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Seed geometric parameters are important in yielding trait scorers, quantitative trait loci, and species recognition and classification. A novel method for automatic measurement of three-dimensional seed phenotypes is proposed. First, a handheld three-dimensional (3D) laser scanner is employed to obtain the seed point cloud data in batches. Second, a novel point cloud-based phenotyping method is proposed to obtain a single-seed 3D model and extract 33 phenotypes. It is connected by an automatic pipeline, including single-seed segmentation, pose normalization, point cloud completion by an ellipse fitting method, Poisson surface reconstruction, and automatic trait estimation. Finally, two statistical models (one using 11 size-related phenotypes and the other using 22 shape-related phenotypes) based on the principal component analysis method are built. A total of 3400 samples of eight kinds of seeds with different geometrical shapes are tested. Experiments show: (1) a single-seed 3D model can be automatically obtained with 0.017 mm point cloud completion error; (2) 33 phenotypes can be automatically extracted with high correlation compared with manual measurements (correlation coefficient (R2) above 0.9981 for size-related phenotypes and R2 above 0.8421 for shape-related phenotypes); and (3) two statistical models are successfully built to achieve seed shape description and quantification. [ABSTRACT FROM AUTHOR]

Polydopamine (PDA) is discovered as a biopolymer that obtained by oxide polymerization of dopamine (DA) in alkaline environment. PDA has unique characteristics such as exceptional adhesiveness, metal chelation, and excellent biocompatibility. In recent years, PDA has been extensively utilized in various area such as biomedicine, materials science and food technology, etc. In present work, different methods of PDA preparation are summarized such as enzymatic oxidation, electro-polymerization and chemical oxidation, and the mechanism of PDA polymerization is discussed using covalent polymerization and supermolecule aggregates. Accordingly, the different self-polymerized PDA nanostructures are classified as nano-coating, nanoparticles, nano-capsules, mesoporous and porous PDA to analysis their advances in functional food packaging, immobilized enzyme, food detection and active substance delivery. In addition, the existing problems and research directions are prospected in order to provide reference for further research of PDA. [ABSTRACT FROM AUTHOR]

Accurate and efficient three-dimensional (3D) streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks. Recently, remarkable progress has been made in streetscape reconstruction with visual images and light detection and ranging (LiDAR), but they have difficulties either in scaling and reconstructing large-scale outdoors or in efficient processing. To address these issues, this paper proposed an automatic method for incremental dense reconstruction of large-scale 3D streetscapes from coarse to fine at near real time. Firstly, the pose of vehicle is estimated by visual and laser odometry (VLO) and the state-of-the-art pyramid stereo matching network (PSMNet) is introduced to estimate depth information. Then, incremental dense 3D streetscape reconstruction is conducted by key-frame selection and coarse registration with local optimization. Finally, redundant and noise points are removed through multiple filtering, resulting good quality of dense reconstruction. Comprehensive experiments were undertaken to check the visual effect, trajectory pose error and multi-scale model to model cloud comparison (M3C2) based on reference trajectories and reconstructions provided by the state-of-the-art method, showing the precision, recall and F-score of sampling core points (SCPs) are over 80.42%, 71.68% and 77.19%, respectively, which verified the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking. [ABSTRACT FROM AUTHOR]