Multilayered Fe3O4@(ZIF-8)3 combined with a computer-vision-enhanced immunosensor for chloramphenicol enrichment and detection.

The misuse and overuse of chloramphenicol poses severe threats to food safety and human health. In this work, we developed a magnetic solid-phase extraction (MSPE) pretreatment material coated with a multilayered metal-organic framework (MOF), Fe3O4 @ (ZIF-8)3, for the separation and enrichment of chloramphenicol from fish. Furthermore, we designed an artificial-intelligence-enhanced single microsphere immunosensor. The inherent ultra-high porosity of the MOF and the multilayer assembly strategy allowed for efficient chloramphenicol enrichment (4.51 mg/g within 20 min). Notably, Fe3O4 @ (ZIF-8)3 exhibits a 39.20% increase in adsorption capacity compared to Fe3O4 @ZIF-8. Leveraging the remarkable decoding abilities of artificial intelligence, we achieved the highly sensitive detection of chloramphenicol using a straightforward procedure without the need for specialized equipment, obtaining a notably low detection limit of 46.42 pM. Furthermore, the assay was successfully employed to detect chloramphenicol in fish samples with high accuracy. The developed immunosensor offers a robust point-of-care testing tool for safeguarding food safety and public health.

Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.

Introduction: Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we propose a dynamic 3D data acquisition method in the field suitable for various crops by using a consumer-grade RGB-D camera installed on a ground-based movable platform, which can collect RGB images as well as depth images of crop canopy sequences dynamically. Methods: A scale-invariant feature transform (SIFT) operator was used to detect adjacent date frames acquired by the RGB-D camera to calculate the point cloud alignment coarse matching matrix and the displacement distance of adjacent images. The data frames used for point cloud matching were selected according to the calculated displacement distance. Then, the colored ICP (iterative closest point) algorithm was used to determine the fine matching matrix and generate point clouds of the crop row. The clustering method was applied to segment the point cloud of each plant from the crop row point cloud, and 3D phenotypic traits, including plant height, leaf area and projected area of individual plants, were measured. Results and Discussion: We compared the effects of LIDAR and image-based 3D reconstruction methods, and experiments were carried out on corn, tobacco, cottons and Bletilla striata in the seedling stage. The results show that the measurements of the plant height (R²= 0.9~0.96, RSME = 0.015~0.023 m), leaf area (R²= 0.8~0.86, RSME = 0.0011~0.0041 m 2 ) and projected area (R² = 0.96~0.99) have strong correlations with the manual measurement results. Additionally, 3D reconstruction results with different moving speeds and times throughout the day and in different scenes were also verified. The results show that the method can be applied to dynamic detection with a moving speed up to 0.6 m/s and can achieve acceptable detection results in the daytime, as well as at night. Thus, the proposed method can improve the efficiency of individual crop 3D point cloud data extraction with acceptable accuracy, which is a feasible solution for crop seedling 3D phenotyping outdoors.

Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging.

Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 s per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The best accuracy for indica and japonica identification was 99.950%, while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

Frontally placed eyes versus laterally placed eyes: computational comparison of their functions for ego-motion estimation.

Both frontally placed eyes and laterally placed eyes are popular in nature, and although which one is better could be one of the most intuitive questions to ask, it could also be the hardest question to answer. Their most obvious difference is that, at least as supposed in the computer vision community, stereopsis plays the central role in the visual system composed of frontally placed eyes (or cameras); however, it is not available in the lateral configuration due to the lack of overlap between the visual fields. As a result, researchers have adopted completely different approaches to model the two configurations and developed computational mimics of them to address various vision problems. Recently, the advent of novel quasi-parallax conception unifies the ego-motion estimation procedure of these two eye configurations into the same framework and makes systematic comparison feasible. In this paper, we intend to establish the computational superiority of eye topography from the perspective of ego-motion estimation. Specifically, quasi-parallax is applied to fuse motion cues from individual cameras at an early stage, at the pixel level, and to recover the translation and rotation separately with high accuracy and efficiency without the need of feature matching. Furthermore, its applicability on the extended sideways arrangements is studied successfully to make our comparison more general and insightful. Extensive experiments on both synthetic and real data have been done, and the computational superiority of the lateral configuration is verified.