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Paddy field object detection for robotic combine based on real‐time semantic segmentation algorithm

https://doi.org/10.1002/rob.22260
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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.21869openalex-snapshot
Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture
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/10.1002/rob.22124openalex-snapshot
Research on fault diagnosis of bearings in walking part of wall‐building robot based on roadside acoustic signal
inferredcites →https://doi.org/10.1007/978-3-030-01219-9_25openalex-snapshot
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_1openalex-snapshot
CBAM: Convolutional Block Attention Module
inferredcites →https://doi.org/10.1007/978-3-030-01261-8_20openalex-snapshot
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation
inferredcites →https://doi.org/10.1007/978-3-030-01264-9_8openalex-snapshot
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
inferredcites →https://doi.org/10.1007/978-3-319-46493-0_32openalex-snapshot
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
inferredcites →https://doi.org/10.1016/j.autcon.2022.104412openalex-snapshot
Attention-based generative adversarial network with internal damage segmentation using thermography
inferredcites →https://doi.org/10.1016/j.biosystemseng.2020.01.018openalex-snapshot
Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging
inferredcites →https://doi.org/10.1016/j.compag.2012.08.011openalex-snapshot
Human detection for a robot tractor using omni-directional stereo vision
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.2020.105274openalex-snapshot
Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia
inferredcites →https://doi.org/10.1016/j.neucom.2022.01.005openalex-snapshot
Review the state-of-the-art technologies of semantic segmentation based on deep learning
inferredcites →https://doi.org/10.1038/s41598-023-28530-2openalex-snapshot
Dual encoder–decoder-based deep polyp segmentation network for colonoscopy images
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for 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.2017.195openalex-snapshot
Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2017.660openalex-snapshot
Pyramid Scene Parsing Network
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2019.00975openalex-snapshot
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00135openalex-snapshot
SwiftNet: Real-time Video Object Segmentation
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/iccv.2019.00140openalex-snapshot
Searching for MobileNetV3
inferredcites →https://doi.org/10.1109/icip.2019.8803154openalex-snapshot
Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
inferredcites →https://doi.org/10.1109/tie.2019.2945265openalex-snapshot
SDDNet: Real-Time Crack Segmentation
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.1177/0018720817738591openalex-snapshot
Falls From Agricultural Machinery: Risk Factors Related to Work Experience, Worked Hours, and Operators’ Behavior
inferredcites →https://doi.org/10.3182/20130828-2-sf-3019.00048openalex-snapshot
A Method to Detect the Occurrence of Rice Plant Lodging Using Wavelet Transform
inferredcites →https://doi.org/10.3390/agronomy10040590openalex-snapshot
Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision
inferredcites →https://doi.org/10.3390/s20092610openalex-snapshot
A SVM and SLIC Based Detection Method for Paddy Field Boundary Line
inferredcites →https://doi.org/10.48550/arxiv.1404.0736openalex-snapshot
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
inferredcites →https://doi.org/10.48550/arxiv.1602.02830openalex-snapshot
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
inferredcites →https://doi.org/10.48550/arxiv.2204.02681openalex-snapshot
PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
inferredcites →https://doi.org/10.52202/068431-0724openalex-snapshot
GhostNetV2: Enhance Cheap Operation with Long-Range Attention

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.4864807openalex-extracted
Rice Lodging Mapping Generated by Lidar and Gnss for Robot Combine
inferred← citeshttps://doi.org/10.1016/j.atech.2026.101894openalex-snapshot
Real-time deep learning segmentation for agricultural applications: Insights for precision weed management
inferred← citeshttps://doi.org/10.20965/jrm.2026.p0372openalex-snapshot
Agricultural Robots in Open Field
inferred← citeshttps://doi.org/10.3389/fpls.2024.1434968openalex-snapshot
MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing
inferred← citeshttps://doi.org/10.3390/agriculture14071122openalex-snapshot
TCNet: Transformer Convolution Network for Cutting-Edge Detection of Unharvested Rice Regions
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