On the public citation graph
Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields
0 signed · 191 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-46448-0_2openalex-snapshot
SSD: Single Shot MultiBox Detector
inferredcites →https://doi.org/10.1007/s11119-008-9097-6openalex-snapshot
Beyond patch spraying: site-specific weed management with several herbicides
inferredcites →https://doi.org/10.1007/s11119-012-9276-3openalex-snapshot
Socioeconomic impact of widespread adoption of precision farming and controlled traffic systems in Denmark
inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot
Selective Search for Object Recognition
inferredcites →https://doi.org/10.1007/s13593-016-0405-7openalex-snapshot
Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery
inferredcites →https://doi.org/10.1016/j.asoc.2010.01.011openalex-snapshot
A computer vision approach for weeds identification through Support Vector Machines
inferredcites →https://doi.org/10.1016/j.compag.2015.12.016openalex-snapshot
Weed detection using image processing under different illumination for site-specific areas spraying
inferredcites →https://doi.org/10.1016/j.compag.2018.02.016openalex-snapshot
Deep learning in agriculture: A survey
inferredcites →https://doi.org/10.1016/j.cropro.2014.05.016openalex-snapshot
Effects of crop establishment techniques on weeds and rice yield
inferredcites →https://doi.org/10.1016/j.cropro.2016.08.031openalex-snapshot
Weed management challenges in rice (Oryza sativa L.) for food security in Bhutan: A review
inferredcites →https://doi.org/10.1016/j.eswa.2015.10.043openalex-snapshot
Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery
inferredcites →https://doi.org/10.1109/cvpr.2014.276openalex-snapshot
Scalable Object Detection Using Deep Neural Networks
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.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7299024openalex-snapshot
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
inferredcites →https://doi.org/10.1109/cvpr.2016.343openalex-snapshot
Instance-Aware Semantic Segmentation via Multi-task Network Cascades
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/iccv.2015.178openalex-snapshot
Learning Deconvolution Network for Semantic Segmentation
inferredcites →https://doi.org/10.1109/icpr.2016.7900179openalex-snapshot
MRCNN: A stateful Fast R-CNN
inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1371/journal.pone.0196302openalex-snapshot
A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery
inferredcites →https://doi.org/10.3390/s150819688openalex-snapshot
Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping
inferredcites →https://doi.org/10.3390/s18072113openalex-snapshot
A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery
inferredcites →https://doi.org/10.3390/s18103299openalex-snapshot
Accurate Weed Mapping and Prescription Map Generation Based on Fully Convolutional Networks Using UAV Imagery
inferredcites →https://doi.org/10.3897/nl.37.7708openalex-snapshot
The biology of Gynnidomorpha permixtana (Lepidoptera, Tortricidae) on Sagittaria trifolia L. (Alismataceae) in paddy fields in Iran
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
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.3959386openalex-extracted
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
inferred← citeshttps://doi.org/10.2139/ssrn.4641592openalex-extracted
Multi-Crop-Row Perception Method Based on Improved Deeplabv3+ and R-Lsm
inferred← citeshttps://doi.org/10.1007/978-3-031-35248-5_52openalex-snapshot
Review of Weed Detection Methods Based on Machine Learning Models
inferred← citeshttps://doi.org/10.1007/978-3-032-21164-4_38openalex-snapshot
Performance Analysis of Supervised Learning Algorithms for Real-Time Weed Detection: A Comparative Study
inferred← citeshttps://doi.org/10.1007/978-981-16-7985-8_51openalex-snapshot
Automation of Rice Cultivation from Ploughing–Harvesting with Diseases, Pests and Weeds to Increase the Yield Using AI
inferred← citeshttps://doi.org/10.1007/978-981-96-9554-6_29openalex-snapshot
Comparative Study of Weed Detection Techniques Using Machine and Deep Learning for Sustainable Agriculture
inferred← citeshttps://doi.org/10.1007/978-981-97-0037-0_24openalex-snapshot
A Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture
inferred← citeshttps://doi.org/10.1007/978-981-97-2508-3_1openalex-snapshot
Weed Localization: Comparison of Different Transfer Learning Models with U-Net
inferred← citeshttps://doi.org/10.1007/978-981-99-3481-2_10openalex-snapshot
A Cascaded Deep Learning Approach for Detection and Localization of Crop-Weeds in RGB Images
inferred← citeshttps://doi.org/10.1007/978-981-99-5881-8_13openalex-snapshot
Hybrid Pre-trained CNN for Multi-classification of Rice Plants
inferred← citeshttps://doi.org/10.1007/s11042-024-18272-2openalex-snapshot
A self-supervised overlapped multiple weed and crop leaf segmentation approach under complex light condition
inferred← citeshttps://doi.org/10.1007/s11119-022-09954-8openalex-snapshot
Early/late fusion structures with optimized feature selection for weed detection using visible and thermal images of paddy fields
inferred← citeshttps://doi.org/10.1007/s11760-023-02863-xopenalex-snapshot
Weed detection in agricultural fields via automatic graph cut segmentation with Mobile Net classification model
inferred← citeshttps://doi.org/10.1007/s12524-025-02283-zopenalex-snapshot
Weed Detection Using Deep Learning in Rice Crops Under Field Conditions
inferred← citeshttps://doi.org/10.1007/s41348-022-00663-yopenalex-snapshot
Multilayer feature fusion and attention-based network for crops and weeds segmentation
inferred← citeshttps://doi.org/10.1007/s41348-025-01209-8openalex-snapshot
Automatic weed detection in paddy field: a review
inferred← citeshttps://doi.org/10.1007/s43154-022-00086-5openalex-snapshot
Review of Current Robotic Approaches for Precision Weed Management
inferred← citeshttps://doi.org/10.1007/s44196-022-00129-xopenalex-snapshot
An Efficient Plant Disease Recognition System Using Hybrid Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) for Smart IoT Applications in Agriculture
inferred← citeshttps://doi.org/10.1016/j.aiia.2023.09.002openalex-snapshot
Machine learning for weed–plant discrimination in agriculture 5.0: An in-depth review
inferred← citeshttps://doi.org/10.1016/j.aiia.2024.06.005openalex-snapshot
A comprehensive survey on weed and crop classification using machine learning and deep learning
inferred← citeshttps://doi.org/10.1016/j.apenergy.2026.127378openalex-snapshot
A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.113662openalex-snapshot
Artificial intelligence-based semi-supervised crop and weed semantic segmentation
inferred← citeshttps://doi.org/10.1016/j.atech.2023.100337openalex-snapshot
Smart spraying technologies for precision weed management: A review
inferred← citeshttps://doi.org/10.1016/j.atech.2024.100505openalex-snapshot
New segmentation approach for effective weed management in agriculture
inferred← citeshttps://doi.org/10.1016/j.atech.2024.100557openalex-snapshot
PIS-Net: Efficient weakly supervised instance segmentation network based on annotated points for rice field weed identification
inferred← citeshttps://doi.org/10.1016/j.atech.2024.100761openalex-snapshot
Taxonomic hierarchical loss function for enhanced crop and weed phenotyping in multi-task semantic segmentation
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101405openalex-snapshot
Advances on deep learning for proximal image-based weed recognition and control under authentic farmlands: A state-of-the-art review
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101619openalex-snapshot
Cotton seedling monitoring and growth stage classification integrating deep learning and feature engineering
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102160openalex-snapshot
A DRCF-YOLO based method for rice seedling detection and density distribution mapping in UAV imagery
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2022.12.012openalex-snapshot
Rice seedling row detection based on morphological anchor points of rice stems
inferred← citeshttps://doi.org/10.1016/j.cj.2021.03.015openalex-snapshot
High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105446openalex-snapshot
Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105590openalex-snapshot
Weed density classification in rice crop using computer vision
inferred← citeshttps://doi.org/10.1016/j.compag.2021.106067openalex-snapshot
A survey of deep learning techniques for weed detection from images
inferred← citeshttps://doi.org/10.1016/j.compag.2021.106178openalex-snapshot
An automatic approach for detecting seedlings per hill of machine-transplanted hybrid rice utilizing machine vision
inferred← citeshttps://doi.org/10.1016/j.compag.2022.107146openalex-snapshot
MTS-CNN: Multi-task semantic segmentation-convolutional neural network for detecting crops and weeds
inferred← citeshttps://doi.org/10.1016/j.compag.2022.107179openalex-snapshot
Weed detection in paddy field using an improved RetinaNet network
inferred← citeshttps://doi.org/10.1016/j.compag.2023.107881openalex-snapshot
Attention-Aided Semantic Segmentation Network for Weed Identification in Pineapple Field
inferred← citeshttps://doi.org/10.1016/j.compag.2023.107994openalex-snapshot
Instance segmentation method for weed detection using UAV imagery in soybean fields
inferred← citeshttps://doi.org/10.1016/j.compag.2023.108235openalex-snapshot
Fusing vegetation index and ridge segmentation for robust vision based autonomous navigation of agricultural robots in vegetable farms
inferred← citeshttps://doi.org/10.1016/j.compag.2024.108862openalex-snapshot
SC-Net: A new strip convolutional network model for rice seedling and weed segmentation in paddy field
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109499openalex-snapshot
Review of weed recognition: A global agriculture perspective
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110294openalex-snapshot
Multimodal weed infestation rate prediction framework for efficient farmland management
inferred← citeshttps://doi.org/10.1016/j.compag.2025.111167openalex-snapshot
Recent developments and applications of weed detection, invasion prediction and early warning: A review
inferred← citeshttps://doi.org/10.1016/j.cropro.2023.106522openalex-snapshot
Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning
inferred← citeshttps://doi.org/10.1016/j.cropro.2024.107075openalex-snapshot
Enhancing site-specific weed detection using deep learning transformer architectures
inferred← citeshttps://doi.org/10.1016/j.cropro.2024.107076openalex-snapshot
Advancing precision agriculture with computer vision: A comparative study of YOLO models for weed and crop recognition
inferred← citeshttps://doi.org/10.1016/j.dib.2024.111118openalex-snapshot
RiGaD: An aerial dataset of rice seedlings for assessing germination rates and density
inferred← citeshttps://doi.org/10.1016/j.eja.2023.126845openalex-snapshot
Maize Seedling Information Extraction from Uav Images Based on Semi-Automatic Sample Generation and Mask R-Cnn Model
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109487openalex-snapshot
CNCAN: Contrast and normal channel attention network for super-resolution image reconstruction of crops and weeds
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.113329openalex-snapshot
An enhanced segmentation network built upon the you only look once framework for precise weed recognition in early-stage cotton
inferred← citeshttps://doi.org/10.1016/j.fcr.2025.110236openalex-snapshot
Can convolutional neural networks support agronomic analysis of cereal–legume canopy cover dynamics?
inferred← citeshttps://doi.org/10.1016/j.gltp.2021.08.063openalex-snapshot
Detection of solitary ocean internal waves from SAR images by using U-Net and KDV solver technique
inferred← citeshttps://doi.org/10.1016/j.inpa.2019.12.002openalex-snapshot
Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network
inferred← citeshttps://doi.org/10.1016/j.jafr.2025.102516openalex-snapshot
Review of current robotic approaches for weed management in paddy cultivation
inferred← citeshttps://doi.org/10.1016/j.jag.2021.102553openalex-snapshot
Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data
inferred← citeshttps://doi.org/10.1016/j.jksuci.2023.03.023openalex-snapshot
Multi-level feature re-weighted fusion for the semantic segmentation of crops and weeds
inferred← citeshttps://doi.org/10.1016/j.phycom.2023.102079openalex-snapshot
Unmanned aerial vehicles assisted rice seedling detection using shark smell optimization with deep learning model
inferred← citeshttps://doi.org/10.1016/j.plaphe.2025.100098openalex-snapshot
KDOSS-net: Knowledge distillation-based outpainting and semantic segmentation network for crop and weed images
inferred← citeshttps://doi.org/10.1016/j.suscom.2022.100759openalex-snapshot
Deep learning-based precision agriculture through weed recognition in sugar beet fields
inferred← citeshttps://doi.org/10.1017/wet.2022.84openalex-snapshot
Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale cropping systems
inferred← citeshttps://doi.org/10.1017/wet.2024.7openalex-snapshot
Simulation-based nozzle density optimization for maximized efficacy of a machine vision–based weed control system for applications in turfgrass settings
inferred← citeshttps://doi.org/10.1038/s41598-024-81010-zopenalex-snapshot
Egg mass classification considering the hatching process of Pomacea canaliculata
inferred← citeshttps://doi.org/10.1038/s41598-025-15380-3openalex-snapshot
Detection of weeds in teff crops using deep learning and UAV imagery for precision herbicide application
inferred← citeshttps://doi.org/10.1063/5.0113668openalex-snapshot
Determining paddy crop health from aerial image using machine learning approach: A Brunei Darussalam based study
inferred← citeshttps://doi.org/10.1063/5.0198355openalex-snapshot
Comparison of BPNN and SRNN based automatic weed detection with improved accuracy in wheat, chickpea field images for increase in production and selective herbicide using MATLAB software - A simulation study
inferred← citeshttps://doi.org/10.1063/5.0338221openalex-snapshot
Analyzing the impact of CNN depth on crop weed detection: A comparative study of one to five layer architectures
inferred← citeshttps://doi.org/10.1080/09064710.2021.2011395openalex-snapshot
RETRACTED ARTICLE: An unique model for weed and paddy detection using regional convolutional neural networks
inferred← citeshttps://doi.org/10.1080/10095020.2022.2144770openalex-snapshot
Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning
inferred← citeshttps://doi.org/10.1080/22797254.2024.2352386openalex-snapshot
Images and CNN applications in smart agriculture
inferred← citeshttps://doi.org/10.1080/23311916.2021.2018791openalex-snapshot
Classification of paddy crop and weeds using semantic segmentation
inferred← citeshttps://doi.org/10.1093/g3journal/jkae026openalex-snapshot
GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa
inferred← citeshttps://doi.org/10.1101/2022.06.08.495082openalex-snapshot
GWAS identifies candidate regulators of in planta regeneration in Populus trichocarpa
inferred← citeshttps://doi.org/10.1109/access.2019.2954587openalex-snapshot
Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features
inferred← citeshttps://doi.org/10.1109/access.2020.2987642openalex-snapshot
Deep Neural Network-Based System for Autonomous Navigation in Paddy Field
inferred← citeshttps://doi.org/10.1109/access.2020.3030112openalex-snapshot
U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery
inferred← citeshttps://doi.org/10.1109/access.2023.3242604openalex-snapshot
Saliency-Based Semantic Weeds Detection and Classification Using UAV Multispectral Imaging
inferred← citeshttps://doi.org/10.1109/access.2023.3300234openalex-snapshot
Dense Multi-Scale Convolutional Network for Plant Segmentation
inferred← citeshttps://doi.org/10.1109/cict67193.2025.11399080openalex-snapshot
AgriSeg: A Comprehensive Study on Semantic Segmentation for Leaf Disease, Fruit, and Crop-Weed Detection
inferred← citeshttps://doi.org/10.1109/csnt54456.2022.9787601openalex-snapshot
Segmentation of Rice Seedling using Deep Learning Algorithm
inferred← citeshttps://doi.org/10.1109/cvprw63382.2024.00542openalex-snapshot
Improved Crop and Weed Detection with Diverse Data Ensemble Learning
inferred← citeshttps://doi.org/10.1109/discover52564.2021.9663729openalex-snapshot
Classification of Crop and Weed from Digital Images: A Review
inferred← citeshttps://doi.org/10.1109/i-smac58438.2023.10290653openalex-snapshot
Detection of Weed plant in Farm and Removing using Unmanned Ground Vehicle
inferred← citeshttps://doi.org/10.1109/icais50930.2021.9395986openalex-snapshot
A Detailed Review on Challenges and Imperatives of Various CNN Algorithms in Weed Detection
inferred← citeshttps://doi.org/10.1109/iccae56788.2023.10111357openalex-snapshot
Plant Seedling Classification Using Preprocessed Deep CNN
inferred← citeshttps://doi.org/10.1109/iccisc52257.2021.9485001openalex-snapshot
Weed Detection in Broad Leaves using Invariant U-Net Model
inferred← citeshttps://doi.org/10.1109/iccvw60793.2023.00065openalex-snapshot
Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification
inferred← citeshttps://doi.org/10.1109/icesc48915.2020.9155701openalex-snapshot
Weed Seedling Detection Using Mask Regional Convolutional Neural Network
inferred← citeshttps://doi.org/10.1109/icrais59684.2023.10367086openalex-snapshot
Detecting Crops and Weeds in Fields Using YOLOv6 and Faster R-CNN Object Detection Models
inferred← citeshttps://doi.org/10.1109/sceecs61402.2024.10482324openalex-snapshot
Object Detection approach for Crop and Weed Identification based on Deep Learning
inferred← citeshttps://doi.org/10.1155/2020/6474536openalex-snapshot
Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach
inferred← citeshttps://doi.org/10.1155/2022/6871085openalex-snapshot
A Novel AI-Based Approach for Better Segmentation of the Fungal and Bacterial Leaf Diseases of Rice Plant
inferred← citeshttps://doi.org/10.1186/s13007-023-01015-zopenalex-snapshot
Identifying and mapping individual medicinal plant Lamiophlomis rotata at high elevations by using unmanned aerial vehicles and deep learning
inferred← citeshttps://doi.org/10.11922/11-6035.csd.2025.0169.zhopenalex-snapshot
A dataset of dragon fruit tree identification and small-sample semantic segmentation based on ultra-low altitude remote sensing
inferred← citeshttps://doi.org/10.13031/aea.14495openalex-snapshot
Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network
inferred← citeshttps://doi.org/10.13031/trans.14085openalex-snapshot
Frontier: Autonomy in Detection, Actuation, and Planning for Robotic Weeding Systems
inferred← citeshttps://doi.org/10.1371/journal.pone.0239591openalex-snapshot
A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops
inferred← citeshttps://doi.org/10.1371/journal.pone.0241528openalex-snapshot
High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network
inferred← citeshttps://doi.org/10.14569/ijacsa.2023.0140265openalex-snapshot
An Efficient Real-Time Weed Detection Technique using YOLOv7
inferred← citeshttps://doi.org/10.15587/1729-4061.2021.246706openalex-snapshot
Development of a weed detection system using machine learning and neural network algorithms
inferred← citeshttps://doi.org/10.18280/ria.360503openalex-snapshot
Enhanced Hybrid Neural Networks (CoAtNet) for Paddy Crops Disease Detection and Classification
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1851240/v1openalex-snapshot
Self-Supervised Overlapped Multiple Weed and Crop Species Leaf Segmentation under Complex Light Condition
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3271314/v1openalex-snapshot
Channel Coordination Attention for Crop and Weed Segmentation Neural Networks
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3271314/v2openalex-snapshot
Channel Coordination Attention for Crop and Weed Segmentation Neural Networks
inferred← citeshttps://doi.org/10.2139/ssrn.4869358openalex-snapshot
Weed Identification Using Machine Learning and Deep Learning: A Review
inferred← citeshttps://doi.org/10.23919/sice56594.2022.9905856openalex-snapshot
Egg Masses Classification of Golden Apple Snail Considering Incubation Process
inferred← citeshttps://doi.org/10.25165/j.ijabe.20231604.8364openalex-snapshot
Review of deep learning-based weed identification in crop fields
inferred← citeshttps://doi.org/10.32604/cmc.2023.038796openalex-snapshot
Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture
inferred← citeshttps://doi.org/10.32604/cmc.2025.064007openalex-snapshot
Design a Computer Vision Approach to Localize, Detect and Count Rice Seedlings Captured by a UAV-Mounted Camera
inferred← citeshttps://doi.org/10.32604/csse.2023.027647openalex-snapshot
Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture
inferred← citeshttps://doi.org/10.3389/fcomp.2025.1551326openalex-snapshot
UAV-based estimation of post-sowing rice plant density using RGB imagery and deep learning across multiple altitudes
inferred← citeshttps://doi.org/10.3389/fpls.2019.01404openalex-snapshot
Learning Semantic Graphics Using Convolutional Encoder–Decoder Network for Autonomous Weeding in Paddy
inferred← citeshttps://doi.org/10.3389/fpls.2021.591333openalex-snapshot
DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation
inferred← citeshttps://doi.org/10.3389/fpls.2022.837726openalex-snapshot
Evaluating Cross-Applicability of Weed Detection Models Across Different Crops in Similar Production Environments
inferred← citeshttps://doi.org/10.3389/fpls.2022.927368openalex-snapshot
Semi-supervised Learning for Weed and Crop Segmentation Using UAV Imagery
inferred← citeshttps://doi.org/10.3389/fpls.2023.1133969openalex-snapshot
A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control
inferred← citeshttps://doi.org/10.3389/fpls.2023.1320448openalex-snapshot
Attention-aided lightweight networks friendly to smart weeding robot hardware resources for crops and weeds semantic segmentation
inferred← citeshttps://doi.org/10.3389/fpls.2024.1361002openalex-snapshot
Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review
inferred← citeshttps://doi.org/10.3389/fpls.2024.1449514openalex-snapshot
An improved U-net and attention mechanism-based model for sugar beet and weed segmentation
inferred← citeshttps://doi.org/10.3389/fpls.2025.1507442openalex-snapshot
Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field
inferred← citeshttps://doi.org/10.3389/fpls.2025.1746406openalex-snapshot
Deep learning–based approaches for weed detection in crops
inferred← citeshttps://doi.org/10.3390/agriculture13061176openalex-snapshot
MSFCA-Net: A Multi-Scale Feature Convolutional Attention Network for Segmenting Crops and Weeds in the Field
inferred← citeshttps://doi.org/10.3390/agriculture15050521openalex-snapshot
Research on Precise Segmentation and Center Localization of Weeds in Tea Gardens Based on an Improved U-Net Model and Skeleton Refinement Algorithm
inferred← citeshttps://doi.org/10.3390/agronomy14102253openalex-snapshot
Laser Weeding Technology in Cropping Systems: A Comprehensive Review
inferred← citeshttps://doi.org/10.3390/agronomy16131291openalex-snapshot
Reframing Weed Detection: From Feature-Based Vision to Crop-Guided Intelligence in Precision Agriculture
inferred← citeshttps://doi.org/10.3390/app15137440openalex-snapshot
Ensemble Learning-Based Weed Detection from a Duck’s Perspective Using an Aquatic Drone in Rice Paddies
inferred← citeshttps://doi.org/10.3390/asi9050095openalex-snapshot
Agricultural Intelligence: A Technical Review Within the Perception–Decision–Execution Framework
inferred← citeshttps://doi.org/10.3390/chemosensors10020045openalex-snapshot
Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics
inferred← citeshttps://doi.org/10.3390/drones7100624openalex-snapshot
Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study
inferred← citeshttps://doi.org/10.3390/electronics9101602openalex-snapshot
CED-Net: Crops and Weeds Segmentation for Smart Farming Using a Small Cascaded Encoder-Decoder Architecture
inferred← citeshttps://doi.org/10.3390/pr11041263openalex-snapshot
Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review
inferred← citeshttps://doi.org/10.3390/rs12040633openalex-snapshot
Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
inferred← citeshttps://doi.org/10.3390/rs12193270openalex-snapshot
Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam
inferred← citeshttps://doi.org/10.3390/rs13214486openalex-snapshot
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
inferred← citeshttps://doi.org/10.3390/rs14020274openalex-snapshot
Aerial Imagery Paddy Seedlings Inspection Using Deep Learning
inferred← citeshttps://doi.org/10.3390/rs14174217openalex-snapshot
Real-Time Weed Control Application Using a Jetson Nano Edge Device and a Spray Mechanism
inferred← citeshttps://doi.org/10.3390/rs14215388openalex-snapshot
Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image
inferred← citeshttps://doi.org/10.3390/rs15071817openalex-snapshot
Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field
inferred← citeshttps://doi.org/10.3390/rs15215165openalex-snapshot
Transformer in UAV Image-Based Weed Mapping
inferred← citeshttps://doi.org/10.3390/rs17030454openalex-snapshot
A Survey of Methods for Addressing Imbalance Data Problems in Agriculture Applications
inferred← citeshttps://doi.org/10.3390/s20185249openalex-snapshot
Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields
inferred← citeshttps://doi.org/10.3390/s21113647openalex-snapshot
Review of Weed Detection Methods Based on Computer Vision
inferred← citeshttps://doi.org/10.3390/s21113758openalex-snapshot
Machine Learning in Agriculture: A Comprehensive Updated Review
inferred← citeshttps://doi.org/10.3390/s24134364openalex-snapshot
Memory-Augmented 3D Point Cloud Semantic Segmentation Network for Intelligent Mining Shovels
inferred← citeshttps://doi.org/10.3390/su15075764openalex-snapshot
An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed
inferred← citeshttps://doi.org/10.34133/2020/3194308openalex-snapshot
A Deep Learning-Based Phenotypic Analysis of Rice Root Distribution from Field Images
inferred← citeshttps://doi.org/10.34133/plantphenomics.0031openalex-snapshot
WRA-Net: Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image
inferred← citeshttps://doi.org/10.36227/techrxiv.24204978openalex-snapshot
CroCoS-v1: Enhancing Mask Leakage and Bounding Box Localization for Real-Time Crop/Weed Instance Segmentation
inferred← citeshttps://doi.org/10.36227/techrxiv.24204978.v1openalex-snapshot
Weed Instance Segmentation
inferred← citeshttps://doi.org/10.37391/ijeer.100412openalex-snapshot
A Novel Approach for Identification of Weeds in Paddy By using Deep Learning Techniques
inferred← citeshttps://doi.org/10.37936/ecti-cit.2022164.246857openalex-snapshot
Classification of Weeds of Paddy Fields using Deep Learning
inferred← citeshttps://doi.org/10.3934/math.2024498openalex-snapshot
An enhanced tunicate swarm algorithm with deep-learning based rice seedling classification for sustainable computing based smart agriculture
inferred← citeshttps://doi.org/10.4018/979-8-3693-6452-9.ch009openalex-snapshot
Precision Farming With Automated Weed Detection Using Machine Learning
inferred← citeshttps://doi.org/10.4081/jae.2025.1741openalex-snapshot
Leveraging deep semantic segmentation for assisted weed detection
inferred← citeshttps://doi.org/10.47836/pjst.32.3.13openalex-snapshot
Weed Management Using UAV and Remote Sensing in Malaysia Paddy Field: A Review
inferred← citeshttps://doi.org/10.55250/jo.vnuf.13.2.2024.112-122openalex-snapshot
Tổng quan về ứng dụng của thiết bị bay không người lái trong canh tác trên đồng ruộng
inferred← citeshttps://doi.org/10.5772/acrt.20250137openalex-snapshot
A Survey on Precision Agriculture: Tasks and Techniques
inferred← citeshttps://doi.org/10.59890/ijaamr.v1i4.568openalex-snapshot
Machine Learning Based Weed Detection System
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.