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Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

https://doi.org/10.1109/icra.2018.8460962
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/rob.21675openalex-snapshot
Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming
inferredcites →https://doi.org/10.1007/978-3-319-48036-7_9openalex-snapshot
Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture
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Bayesian classification and unsupervised learning for isolating weeds in row crops
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Verification of color vegetation indices for automated crop imaging applications
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Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model
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An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops
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A survey of image processing techniques for plant extraction and segmentation in the field
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Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/icra.2016.7487717openalex-snapshot
Self-supervised weed detection in vegetable crops using ground based hyperspectral imaging
inferredcites →https://doi.org/10.1109/icra.2016.7487720openalex-snapshot
An effective classification system for separating sugar beets and weeds for precision farming applications
inferredcites →https://doi.org/10.1109/iros.2017.8206403openalex-snapshot
Semi-supervised online visual crop and weed classification in precision farming exploiting plant arrangement
inferredcites →https://doi.org/10.1109/lgrs.2012.2193113openalex-snapshot
Segmentation of Low-Cost Remote Sensing Images Combining Vegetation Indices and Mean Shift
inferredcites →https://doi.org/10.1109/lra.2017.2667039openalex-snapshot
Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics
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/wacv.2014.6835733openalex-snapshot
Plant classification system for crop /weed discrimination without segmentation
inferredcites →https://doi.org/10.1177/0278364917720510openalex-snapshot
Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields

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

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Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision
inferred← citeshttps://doi.org/10.1038/s41597-025-06462-yopenalex-snapshot
The Multi-Sensor and Multi-Temporal Dataset of Multiple Crops for In-Field Phenotyping and Monitoring
inferred← citeshttps://doi.org/10.1038/s41598-021-03858-9openalex-snapshot
OpenWeedLocator (OWL): an open-source, low-cost device for fallow weed detection
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/s41598-025-31285-7openalex-snapshot
Enhancing the weed segmentation in diverse crop fields using computationally effective concatenated attention U-Net with convolutional block attention module
inferred← citeshttps://doi.org/10.1038/s41598-025-32568-9openalex-snapshot
A lightweight and generalizable deep learning framework for early detection of rice leaf diseases in complex field environments
inferred← citeshttps://doi.org/10.1038/s41598-026-45694-9openalex-snapshot
Frost damage segmentation in grapevine organs using YOLOv11s with ASPP and dynamic confidence thresholding
inferred← citeshttps://doi.org/10.1051/itmconf/20257002012openalex-snapshot
Few-shot Learning: Methods and Applications
inferred← citeshttps://doi.org/10.1080/02664763.2022.2150753openalex-snapshot
Rapid online plant leaf area change detection with high-throughput plant image data
inferred← citeshttps://doi.org/10.1080/1343943x.2020.1829490openalex-snapshot
Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm
inferred← citeshttps://doi.org/10.1080/1343943x.2023.2210767openalex-snapshot
Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy
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/10.1101/2021.07.22.453339openalex-snapshot
Segmenting biological specimens from photos to understand the evolution of UV plumage in passerine birds
inferred← citeshttps://doi.org/10.1108/ir-03-2020-0063openalex-snapshot
A survey of energy-efficient motion planning for wheeled mobile robots
inferred← citeshttps://doi.org/10.1109/access.2019.2911709openalex-snapshot
Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge
inferred← citeshttps://doi.org/10.1109/access.2019.2942158openalex-snapshot
Real-Time Crop Recognition in Transplanted Fields With Prominent Weed Growth: A Visual-Attention-Based Approach
inferred← citeshttps://doi.org/10.1109/access.2020.2987341openalex-snapshot
A 3-Stage Machine Learning-Based Novel Object Grasping Methodology
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.2020.2991354openalex-snapshot
Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination
inferred← citeshttps://doi.org/10.1109/access.2020.3015891openalex-snapshot
Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN
inferred← citeshttps://doi.org/10.1109/access.2021.3057912openalex-snapshot
Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning
inferred← citeshttps://doi.org/10.1109/access.2021.3073715openalex-snapshot
End to End Segmentation of Canola Field Images Using Dilated U-Net
inferred← citeshttps://doi.org/10.1109/access.2021.3108003openalex-snapshot
DeepVeg: Deep Learning Model for Segmentation of Weed, Canola, and Canola Flea Beetle Damage
inferred← citeshttps://doi.org/10.1109/access.2021.3109015openalex-snapshot
A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop
inferred← citeshttps://doi.org/10.1109/access.2021.3116814openalex-snapshot
AgriFusion: An Architecture for IoT and Emerging Technologies Based on a Precision Agriculture Survey
inferred← citeshttps://doi.org/10.1109/access.2022.3155531openalex-snapshot
Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images
inferred← citeshttps://doi.org/10.1109/access.2023.3284036openalex-snapshot
PD-SegNet: Semantic Segmentation of Small Agricultural Targets in Complex Environments
inferred← citeshttps://doi.org/10.1109/access.2025.3561325openalex-snapshot
FML-Swin: An Improved Swin Transformer Segmentor for Remote Sensing Images
inferred← citeshttps://doi.org/10.1109/access.2025.3590587openalex-snapshot
Generative Models, Attention Mechanisms, and Adaptive Methods for Robot Navigation in Complex Environments—A Survey
inferred← citeshttps://doi.org/10.1109/access.2026.3729808openalex-snapshot
Crop–Weed Multispectral Semantic Segmentation With Foundation Models and Hardness-Aware Learning
inferred← citeshttps://doi.org/10.1109/access.2026.3730987openalex-snapshot
Crop Segmentation for Precision Agriculture From UAV Imagery Using ML and DL With Sparse Annotations
inferred← citeshttps://doi.org/10.1109/access.2026.3735483openalex-snapshot
MaizeSeg: An Under-Canopy Maize Image Dataset for Vegetation Background Segmentation Enabling Downstream Stress Phenotyping
inferred← citeshttps://doi.org/10.1109/acdsa67686.2026.11467945openalex-snapshot
Toward Grounded YOLO-SAM: Unified Detection–Segmentation Framework for Agricultural Intelligence
inferred← citeshttps://doi.org/10.1109/agreta57740.2023.10262692openalex-snapshot
Semantic Segmentation of Weed and Crop with Partially Annotated Data for Automated Agriculture
inferred← citeshttps://doi.org/10.1109/agro-geoinformatics66479.2025.11136213openalex-snapshot
Adapting Vision-Language Models for Precision Agriculture: A Study on Crop Segmentation based on UAV Remote Sensing Data
inferred← citeshttps://doi.org/10.1109/aicconf64766.2025.11064286openalex-snapshot
Impact of Image Compression on Banana Trunk Detection with YOLOv5 for Mobile Robot Deployment in Banana Plantations
inferred← citeshttps://doi.org/10.1109/aimla59606.2024.10531416openalex-snapshot
Deep Learning-Based Semantic Segmentation of Turmeric Crop Images Using the DeepLabV3+ Architecture
inferred← citeshttps://doi.org/10.1109/aimla67915.2026.11522319openalex-snapshot
Autonomous Agricultural Robot for Crop-Safe Weed Control
inferred← citeshttps://doi.org/10.1109/apscon68325.2026.11497174openalex-snapshot
Real-Time Crop Sensing and Localization for Precision Agriculture
inferred← citeshttps://doi.org/10.1109/auteee60196.2023.10408693openalex-snapshot
Using Improved DeepLabV3+ for Complex Scene Segmentation
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10021091openalex-snapshot
Semantic Segmentation Approaches in Crop Row Detection
inferred← citeshttps://doi.org/10.1109/ccta41146.2020.9206297openalex-snapshot
Data Augmentation Using GANs for Crop/Weed Segmentation in Precision Farming
inferred← citeshttps://doi.org/10.1109/cicn67655.2025.11367978openalex-snapshot
An Explainable AI Approach for Weed and Crop Localization Via Image-Level Classification
inferred← citeshttps://doi.org/10.1109/cis-ram47153.2019.9095778openalex-snapshot
Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection
inferred← citeshttps://doi.org/10.1109/comsci55378.2022.9912593openalex-snapshot
CNN-based Approaches for Weed Detection
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00382openalex-snapshot
Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01087openalex-snapshot
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.01668openalex-snapshot
Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention
inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.00307openalex-snapshot
Open-World Semantic Segmentation Including Class Similarity
inferred← citeshttps://doi.org/10.1109/cvprw56347.2022.00172openalex-snapshot
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
inferred← citeshttps://doi.org/10.1109/cvprw59228.2023.00472openalex-snapshot
Uncertainty in Real-Time Semantic Segmentation on Embedded Systems
inferred← citeshttps://doi.org/10.1109/cvprw67362.2025.00547openalex-snapshot
iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species
inferred← citeshttps://doi.org/10.1109/cyber53097.2021.9588303openalex-snapshot
Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation
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/eee64897.2025.11162898openalex-snapshot
Uniform Clustering Based Unsupervised Domain Adaptation for Semantic Segmentation
inferred← citeshttps://doi.org/10.1109/i2mtc50364.2021.9459976openalex-snapshot
Deep learning techniques for enhancement of weeds growth classification
inferred← citeshttps://doi.org/10.1109/ic-etite58242.2024.10493587openalex-snapshot
Crop Yield Prediction Using Machine Learning Techniques Based on Soil and Environment Traits – A Survey
inferred← citeshttps://doi.org/10.1109/ic3ina48034.2019.8949580openalex-snapshot
Multi-Condition Training on Deep Convolutional Neural Networks for Robust Plant Diseases Detection
inferred← citeshttps://doi.org/10.1109/icabcd54961.2022.9856067openalex-snapshot
Plant Seedling Classification Using Machine Learning
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/icarcv50220.2020.9305345openalex-snapshot
Towards Semantic Scene Segmentation for Autonomous Agricultural Vehicles
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