Fruit Detection and Pose Estimation for Grape Cluster–Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks

Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots. The pose of fruits is crucial to guide robots to approach target fruits for collision-free picking. To achieve accurate picking, this study investigates an approach to detect fruit and estimate its pose. First, the state-of-the-art mask region convolutional neural network (Mask R-CNN) is deployed to segment binocular images to output the mask image of the target fruit. Next, a grape point cloud extracted from the images was filtered and denoised to obtain an accurate grape point cloud. Finally, the accurate grape point cloud was used with the RANSAC algorithm for grape cylinder model fitting, and the axis of the cylinder model was used to estimate the pose of the grape. A dataset was acquired in a vineyard to evaluate the performance of the proposed approach in a nonstructural environment. The fruit detection results of 210 test images show that the average precision, recall, and intersection over union (IOU) are 89.53, 95.33, and 82.00%, respectively. The detection and point cloud segmentation for each grape took approximately 1.7 s. The demonstrated performance of the developed method indicates that it can be applied to grape-harvesting robots

Gastric Subserous Vaccination With Helicobacter pylori Vaccine: An Attempt to Establish Tissue-Resident CD4+ Memory T Cells and Induce Prolonged Protection

Tissue-resident memory T (Trm) cells are enriched at the sites of previous infection and required for enhanced protective immunity. However, the emergence of Trm cells and their roles in providing protection are unclear in the field of Helicobacter pylori (H. pylori) vaccinology. Here, our results suggest that conventional vaccine strategies are unable to establish a measurable antigen (Ag)-specific memory cell pool in stomach; in comparison, gastric subserous injection of mice with micro-dose of Alum-based H. pylori vaccine can induce a pool of local CD4+ Trm cells. Regional recruitment of Ag-specific CD4+ T cells depends on the engagement of Ag and adjuvant-induced inflammation. Prior subcutaneous vaccination enhanced this recruitment. A stable pool of Ag-specific CD4+ T cells can be detected for 240 days. Two weeks of FTY720 administration in immune mice suggests that these cells do not experience the recirculation. Immunohistochemistry results show that close to the vaccination site, abundant CD4+T cells locate on epithelial niches, independent of lymphocyte cluster. Paradigmatically, Ag-specific CD4+ T cells with a phenotype of CD69+CD103- are preferential on lymphocytes isolated from epithelium. Upon Helicobacter infection, CD4+ Trm cells orchestrate a swift recall response with the recruitment of circulating antigen-specific Th1/Th17 cells to trigger a tissue-wide pathogen clearance. This study investigates the vaccine-induced gastric CD4+ Trm cells in a mice model, and highlights the need for designing a vaccine strategy against H. pylori by establishing the protective CD4+ Trm cells

Complete genome sequence of biocontrol strain Bacillus velezensis YC89 and its biocontrol potential against sugarcane red rot

IntroductionSugarcane is one of the most important sugar crops worldwide, however, sugarcane production is seriously limited by sugarcane red rot, a soil-borne disease caused by Colletotrichum falcatum. Bacillus velezensis YC89 was isolated from sugarcane leaves and can significantly inhibited red rot disease caused by C. falcatum.MethodsIn this study, the genome of YC89 strain was sequenced, its genome structure and function were analyzed using various bioinformatics software, and its genome was compared with those of other homologous strains. In addition, the effectiveness of YC89 against sugarcane red rot and the evaluation of sugarcane plant growth promotion were also investigated by pot experiments.ResultsHere, we present the complete genome sequence of YC89, which consists of a 3.95 Mb circular chromosome with an average GC content of 46.62%. The phylogenetic tree indicated that YC89 is closely related to B. velezensis GS-1. Comparative genome analysis of YC89 with other published strains (B. velezensis FZB42, B. velezensis CC09, B. velezensis SQR9, B. velezensis GS-1, and B. amyloliquefaciens DSM7) revealed that the strains had a part common coding sequences (CDS) in whereas 42 coding were unique of strain YC89. Whole-genome sequencing revealed 547 carbohydrate-active enzymes and identified 12 gene clusters encoding secondary metabolites. Additionally, functional analysis of the genome revealed numerous gene/gene clusters involved in plant growth promotion, antibiotic resistance, and resistance inducer synthesis. In vitro pot tests indicated that YC89 strain controlled sugarcane red rot and promoted the growth of sugarcane plants. Additionally, it increased the activity of enzymes involved in plant defense, such as superoxide dismutase, peroxidase, polyphenol oxidase, chitinase, and β-1,3-glucanase.DiscussionThese findings will be helpful for further studies on the mechanisms of plant growth promotion and biocontrol by B. velezensis and provide an effective strategy for controlling red rot in sugarcane plants

Metasurface spectrometers beyond resolution-sensitivity constraints

Optical spectroscopy plays an essential role across scientific research and industry for non-contact materials analysis1-3, increasingly through in-situ or portable platforms4-6. However, when considering low-light-level applications, conventional spectrometer designs necessitate a compromise between their resolution and sensitivity7,8, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector is instead enhanced as the resolution is increased. This planar, CMOS-compatible platform is based around metasurface encoders designed to exhibit photonic bound states in the continuum9, where operational range can be altered or extended simply through adjusting geometric parameters. This system can enhance photon collection efficiency by up to two orders of magnitude versus conventional designs; we demonstrate this sensitivity advantage through ultra-low-intensity fluorescent and astrophotonic spectroscopy. This work represents a step forward for the practical utility of spectrometers, affording a route to integrated, chip-based devices that maintain high resolution and SNR without requiring prohibitively long integration times

YOLOC-tiny: a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head’s efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province—navel orange, Ehime Jelly orange, and Harumi tangerine—YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection

Application of Smart Technology and Equipment in Horticulture

Horticulture, as an important component of modern agriculture, plays a significant role in beautifying the environment but also in enriching human nutrition [...

Research progress on visual perception and end-effecter of undamaged operation for fruit-picking robot

Research and development of fruit-picking robot is of great significance for improving harvest efficiency, guaranteeing fruit quality and reducing labor intensity. However, picking-robot under the effect of non-structural factors including over lapping, adhesion and occlusion between bunched fruits may damage the fruits very easily due to inaccurate target positioning, incorrect clamping-cutting sequence and improper clamping-cutting gesture. The main cause of the damage is that the coupling mechanism between visual cognition and clamping-cutting actuators for undamaged picking has not been well settled. In order to comb the latest research progress on anti damage operation of fruit-picking robot, this paper makes a comprehensive review and analysis on undamaged harvesting from three aspects: the multi-dimensional information of targets by visual perception, such as picking point, peduncle position, bounding volume of grape for undamaged fruit-picking and so on; visual cognition and intelligent anti damage picking planning of fruit-picking robots; anti damage picking mechanism design and its behavior control. Moreover, the key problems that need to be solved in the future are summarized and prospected. This paper may provide reference and views for further research on the problems of fruit intelligent undamaged picking in unstructured environment