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An end-to-end lightweight model for grape and picking point simultaneous detection

https://doi.org/10.1016/j.biosystemseng.2022.08.013
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/rob.21889openalex-snapshot
An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation
inferredcites →https://doi.org/10.1002/rob.21902openalex-snapshot
A comparative study of fruit detection and counting methods for yield mapping in apple orchards
inferredcites →https://doi.org/10.1007/978-3-030-84760-9_24openalex-snapshot
A Review on Fruit Detection and Segmentation Techniques in Agricultural Field
inferredcites →https://doi.org/10.1007/978-3-319-93000-8_12openalex-snapshot
Pixel-Based Leaf Segmentation from Natural Vineyard Images Using Color Model and Threshold Techniques
inferredcites →https://doi.org/10.1007/s11119-018-9586-1openalex-snapshot
Identifying immature and mature pomelo fruits in trees by elliptical model fitting in the Cr–Cb color space
inferredcites →https://doi.org/10.1007/s11947-021-02665-4openalex-snapshot
Green Extraction Methods and Microencapsulation Technologies of Phenolic Compounds From Grape Pomace: A Review
inferredcites →https://doi.org/10.1016/j.aiia.2021.10.001openalex-snapshot
Early stage detection of Downey and Powdery Mildew grape disease using atmospheric parameters through sensor nodes
inferredcites →https://doi.org/10.1016/j.biosystemseng.2017.11.005openalex-snapshot
The recognition of litchi clusters and the calculation of picking point in a nocturnal natural environment
inferredcites →https://doi.org/10.1016/j.biosystemseng.2019.08.016openalex-snapshot
Computer vision-based localisation of picking points for automatic litchi harvesting applications towards natural scenarios
inferredcites →https://doi.org/10.1016/j.biosystemseng.2021.03.012openalex-snapshot
A mango picking vision algorithm on instance segmentation and key point detection from RGB images in an open orchard
inferredcites →https://doi.org/10.1016/j.biosystemseng.2021.11.011openalex-snapshot
A real-time table grape detection method based on improved YOLOv4-tiny network in complex background
inferredcites →https://doi.org/10.1016/j.compag.2015.05.021openalex-snapshot
Sensors and systems for fruit detection and localization: A review
inferredcites →https://doi.org/10.1016/j.compag.2019.06.001openalex-snapshot
Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
inferredcites →https://doi.org/10.1016/j.compag.2019.105192openalex-snapshot
A visual detection method for nighttime litchi fruits and fruiting stems
inferredcites →https://doi.org/10.1016/j.compag.2020.105247openalex-snapshot
Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
inferredcites →https://doi.org/10.1016/j.compag.2020.105475openalex-snapshot
Using color and 3D geometry features to segment fruit point cloud and improve fruit recognition accuracy
inferredcites →https://doi.org/10.1016/j.compag.2021.106220openalex-snapshot
Grape stem detection using regression convolutional neural networks
inferredcites →https://doi.org/10.1016/j.compag.2021.106562openalex-snapshot
Vision systems for harvesting robots: Produce detection and localization
inferredcites →https://doi.org/10.1016/j.compind.2018.03.017openalex-snapshot
A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard
inferredcites →https://doi.org/10.1016/j.eswa.2020.113588openalex-snapshot
Grape detection with convolutional neural networks
inferredcites →https://doi.org/10.1016/j.ifacol.2018.08.054openalex-snapshot
Development of a Robot for Harvesting Strawberries
inferredcites →https://doi.org/10.1016/j.inpa.2021.12.004openalex-snapshot
Tea picking point detection and location based on Mask-RCNN
inferredcites →https://doi.org/10.1016/j.jal.2012.07.004openalex-snapshot
Automatic detection of bunches of grapes in natural environment from color images
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/access.2019.2946267openalex-snapshot
Grasping Point Detection of Randomly Placed Fruit Cluster Using Adaptive Morphology Segmentation and Principal Component Classification of Multiple Features
inferredcites →https://doi.org/10.1109/access.2019.2958614openalex-snapshot
Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model
inferredcites →https://doi.org/10.1109/access.2020.3003034openalex-snapshot
Real-Time Visual Localization of the Picking Points for a Ridge-Planting Strawberry Harvesting Robot
inferredcites →https://doi.org/10.1109/access.2020.3048374openalex-snapshot
End-to-End Automatic Berry Counting for Table Grape Thinning
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00165openalex-snapshot
GhostNet: More Features From Cheap Operations
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00203openalex-snapshot
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
inferredcites →https://doi.org/10.1109/cw49994.2020openalex-snapshot
2020 International Conference on Cyberworlds (CW)
inferredcites →https://doi.org/10.1109/cyber53097.2021openalex-snapshot
2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)
inferredcites →https://doi.org/10.1109/iccv.2017.593openalex-snapshot
Soft-NMS — Improving Object Detection with One Line of Code
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1177/1729881420925310openalex-snapshot
Apple harvesting robot under information technology: A review
inferredcites →https://doi.org/10.20965/jrm.2020.p0437openalex-snapshot
Cutting Point Detection Using a Robot with Point Clouds for Tomato Harvesting
inferredcites →https://doi.org/10.25165/ijabe.v10i1.2517openalex-snapshot
Grape size detection and online gradation based on machine vision
inferredcites →https://doi.org/10.3389/fpls.2020.00510openalex-snapshot
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review
inferredcites →https://doi.org/10.3390/horticulturae7110492openalex-snapshot
SwinGD: A Robust Grape Bunch Detection Model Based on Swin Transformer in Complex Vineyard Environment
inferredcites →https://doi.org/10.3390/joitmc7020123openalex-snapshot
Sensing Technologies, Roles and Technology Adoption Strategies for Digital Transformation of Grape Harvesting in SME Wineries
inferredcites →https://doi.org/10.3390/rs13091619openalex-snapshot
A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5
inferredcites →https://doi.org/10.3390/s19224850openalex-snapshot
Deep Learning Techniques for Grape Plant Species Identification in Natural Images
inferredcites →https://doi.org/10.3390/s20010093openalex-snapshot
An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor
inferredcites →https://doi.org/10.4230/oasics.ng-res.2024.2openalex-snapshot
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.2004.10934openalex-snapshot
YOLOv4: Optimal Speed and Accuracy of Object Detection
inferredcites →https://doi.org/10.5281/zenodo.3361735openalex-snapshot
Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4482152openalex-extracted
A Visual Detection Method for Multiple Kinds of Camellia Oleifera Fruit Picking Robots
inferred← citeshttps://doi.org/10.2139/ssrn.4632625openalex-extracted
Pmgdc: Probability Map-Based Grape Detection and Counting
inferred← citeshttps://doi.org/10.1002/agj2.70535openalex-snapshot
Machine vision techniques for the detection and segmentation of red grape fruits under uncontrolled lighting
inferred← citeshttps://doi.org/10.1002/rob.22518openalex-snapshot
A Universal Visual Detection Method for Camellia oleifera Fruit Picking Robot
inferred← citeshttps://doi.org/10.1007/978-981-97-6441-9_5openalex-snapshot
Recent Advances in Intelligent Harvesting Robots
inferred← citeshttps://doi.org/10.1007/978-981-99-3754-7_13openalex-snapshot
Leveraging Computer Vision for Precision Viticulture
inferred← citeshttps://doi.org/10.1007/s00217-023-04441-4openalex-snapshot
Application of image analysis and machine learning for the assessment of grape (Vitis L.) berry behavior under different storage conditions
inferred← citeshttps://doi.org/10.1007/s00371-026-04567-yopenalex-snapshot
AppleDet: lightweight and robust apple detection in natural orchard environments via reparameterized and state space visual modeling
inferred← citeshttps://doi.org/10.1007/s11119-025-10278-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.aiia.2024.02.001openalex-snapshot
Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments
inferred← citeshttps://doi.org/10.1016/j.aiia.2025.05.004openalex-snapshot
Picking point localization method based on semantic reasoning for complex picking scenarios in vineyards
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101051openalex-snapshot
A lightweight and optimized deep learning model for detecting banana bunches and stalks in autonomous harvesting vehicles
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101062openalex-snapshot
Two–stage multimodal 3D point localization framework for automatic grape harvesting
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101485openalex-snapshot
MangoStem-YOLOv8n: An improved YOLOv8n for mango and stem detection in natural orchard environments
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2023.09.014openalex-snapshot
Table grape inflorescence detection and clamping point localisation based on channel pruned YOLOV7-TP
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2023.12.017openalex-snapshot
Comprehensive visual information acquisition for tomato picking robot based on multitask convolutional neural network
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2024.07.019openalex-snapshot
3D positioning of Camellia oleifera fruit-grabbing points for robotic harvesting
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2024.09.015openalex-snapshot
Positioning of mango picking point using an improved YOLOv8 architecture with object detection and instance segmentation
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2025.104155openalex-snapshot
High-throughput maize seed ears sorting through structural re-parameterisation classification model and multi-channel sorting system
inferred← citeshttps://doi.org/10.1016/j.compag.2023.107923openalex-snapshot
Picking point recognition for ripe tomatoes using semantic segmentation and morphological processing
inferred← citeshttps://doi.org/10.1016/j.compag.2023.108174openalex-snapshot
Grape yield estimation with a smartphone’s colour and depth cameras using machine learning and computer vision techniques
inferred← citeshttps://doi.org/10.1016/j.compag.2023.108203openalex-snapshot
End-to-end lightweight berry number prediction for supporting table grape cultivation
inferred← citeshttps://doi.org/10.1016/j.compag.2023.108316openalex-snapshot
MobileOne-YOLO: Improving the YOLOv7 network for the detection of unfertilized duck eggs and early duck embryo development - a novel approach
inferred← citeshttps://doi.org/10.1016/j.compag.2024.108612openalex-snapshot
Efficient and lightweight grape and picking point synchronous detection model based on key point detection
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109175openalex-snapshot
Probability map-based grape detection and counting
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109216openalex-snapshot
Cognition of grape cluster picking point based on visual knowledge distillation in complex vineyard environment
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109507openalex-snapshot
Assessing cacao beans fermentation degree with improved YOLOv8 instance segmentation
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109725openalex-snapshot
Efficient one-stage location method for grape picking points in natural scene by combining detection network and point regression
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110637openalex-snapshot
Detection and location of wine grape (Cabernet Sauvignon) picking points by using a dual-stage deep learning method
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110814openalex-snapshot
Multi-task perception and three-dimensional picking point localization method for grapes based on structural constraints and geometric analysis
inferred← citeshttps://doi.org/10.1016/j.eng.2025.01.022openalex-snapshot
Advance on Agricultural Robot Hand–Eye Coordination for Agronomic Task: A Review
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.105936openalex-snapshot
Classification of seed corn ears based on custom lightweight convolutional neural network and improved training strategies
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110266openalex-snapshot
Picking point localization method of table grape picking robot based on you only look once version 8 nano
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112580openalex-snapshot
Enhancing precision tomato harvesting with feature sharing and intersection screening
inferred← citeshttps://doi.org/10.1016/j.engappai.2026.116527openalex-snapshot
Tomato picking point detection and three-dimensional pose estimation based on keypoint detection and pedicel skeleton point cloud
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.126220openalex-snapshot
MT-MVSNet: A lightweight and highly accurate convolutional neural network based on mobile transformer for 3D reconstruction of orchard fruit tree branches
inferred← citeshttps://doi.org/10.1016/j.jfutfo.2025.06.003openalex-snapshot
Application of convolutional neural network in the production and processing of flower and fruit tea
inferred← citeshttps://doi.org/10.1016/j.tifs.2024.104730openalex-snapshot
From farm to market: Research progress and application prospects of artificial intelligence in the frozen fruits and vegetables supply chain
inferred← citeshttps://doi.org/10.1088/2631-8695/aea808openalex-snapshot
RrT-YOLO: a scale-adaptive framework for Rosa roxburghii Tratt maturity detection in complex mountainous environments
inferred← citeshttps://doi.org/10.1109/access.2024.3431244openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2025.3545670openalex-snapshot
YOLO-RDM: Innovative Detection Methods for Eggplants and Stems in Complex Natural Environment
inferred← citeshttps://doi.org/10.1109/ivcnz64857.2024.10794487openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ivcnz67716.2025.11281824openalex-snapshot
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inferred← citeshttps://doi.org/10.11922/11-6035.csd.2023.0132.zhopenalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-3178237/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.32604/phyton.2024.052397openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fpls.2024.1395796openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fpls.2025.1614881openalex-snapshot
A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection
inferred← citeshttps://doi.org/10.3390/agriculture12101679openalex-snapshot
A Method for Obtaining the Number of Maize Seedlings Based on the Improved YOLOv4 Lightweight Neural Network
inferred← citeshttps://doi.org/10.3390/agriculture15030271openalex-snapshot
DCFA-YOLO: A Dual-Channel Cross-Feature-Fusion Attention YOLO Network for Cherry Tomato Bunch Detection
inferred← citeshttps://doi.org/10.3390/agriculture15040387openalex-snapshot
RF-YOLOv7: A Model for the Detection of Poor-Quality Grapes in Natural Environments
inferred← citeshttps://doi.org/10.3390/agriculture15151622openalex-snapshot
A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies
inferred← citeshttps://doi.org/10.3390/agriculture16070794openalex-snapshot
YOLOv11n-GrapeLite: A Lightweight Multi-Variety Grape Recognition Model
inferred← citeshttps://doi.org/10.3390/agriculture16121322openalex-snapshot
GL-SeqNet: Global–Local Fusion for Intra-Cluster Tomato Harvesting Sequence Optimization
inferred← citeshttps://doi.org/10.3390/agriculture16171888openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agronomy13061618openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agronomy15010145openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/app16157500openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/horticulturae11091039openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/horticulturae12060697openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/horticulturae9040443openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/plants14030365openalex-snapshot
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inferred← citeshttps://doi.org/10.35633/inmateh-74-42openalex-snapshot
IMPROVED YOLOv8N-BASED DETECTION OF GRAPES IN ORCHARDS
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