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Weed Identification Using Machine Learning and Deep Learning: A Review

https://doi.org/10.2139/ssrn.4869358
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Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots
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Potential use of ground‐based sensor technologies for weed detection
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A novel deep learning‐based method for detection of weeds in vegetables
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Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming
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A Feature-Based Machine Learning Agent for Automatic Rice and Weed Discrimination
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Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture
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Detection and Identification of Weeds
inferredcites →https://doi.org/10.1007/s00500-018-3125-xopenalex-extracted
Research on weeds identification based on K-means feature learning
inferredcites →https://doi.org/10.1007/s10044-012-0307-5openalex-extracted
Bayesian classification and unsupervised learning for isolating weeds in row crops
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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
inferredcites →https://doi.org/10.1007/s11119-005-2324-5openalex-extracted
Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status
inferredcites →https://doi.org/10.1007/s11119-005-6787-1openalex-extracted
Automated Crop and Weed Monitoring in Widely Spaced Cereals
inferredcites →https://doi.org/10.1007/s11119-006-9014-9openalex-extracted
Agricultural robots—system analysis and economic feasibility
inferredcites →https://doi.org/10.1007/s11119-009-9126-0openalex-extracted
Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature
inferredcites →https://doi.org/10.1007/s11119-016-9483-4openalex-extracted
Comparative analysis of texture descriptors in maize fields with plants, soil and object discrimination
inferredcites →https://doi.org/10.1007/s11119-018-09625-7openalex-extracted
Pre-harvest weed mapping of Cirsium arvense in wheat and barley with off-the-shelf UAVs
inferredcites →https://doi.org/10.1007/s11119-021-09808-9openalex-extracted
Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer
inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-extracted
The Pascal Visual Object Classes (VOC) Challenge
inferredcites →https://doi.org/10.1007/s11263-020-01316-zopenalex-extracted
The Open Images Dataset V4
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A new method for gray-level picture thresholding using the entropy of the histogram
inferredcites →https://doi.org/10.1016/bs.agron.2019.08.004openalex-extracted
Unmanned aircraft systems for precision weed detection and management: Prospects and challenges
inferredcites →https://doi.org/10.1016/j.agwat.2014.08.010openalex-extracted
Study and comparison of color models for automatic image analysis in irrigation management applications
inferredcites →https://doi.org/10.1016/j.aiia.2021.03.001openalex-extracted
An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields
inferredcites →https://doi.org/10.1016/j.asoc.2010.01.011openalex-extracted
A computer vision approach for weeds identification through Support Vector Machines
inferredcites →https://doi.org/10.1016/j.atech.2022.100123openalex-extracted
Early weed identification based on deep learning: A review
inferredcites →https://doi.org/10.1016/j.atech.2022.100126openalex-extracted
Performance evaluation of deep learning object detectors for weed detection for cotton
inferredcites →https://doi.org/10.1016/j.atech.2023.100181openalex-extracted
UAV-based weed detection in Chinese cabbage using deep learning
inferredcites →https://doi.org/10.1016/j.biosystemseng.2011.04.003openalex-extracted
Classification of broadleaf weed images using Gabor wavelets and Lie group structure of region covariance on Riemannian manifolds
inferredcites →https://doi.org/10.1016/j.biosystemseng.2014.06.015openalex-extracted
Vegetation segmentation robust to illumination variations based on clustering and morphology modelling
inferredcites →https://doi.org/10.1016/j.biosystemseng.2016.01.014openalex-extracted
Active learning system for weed species recognition based on hyperspectral sensing
inferredcites →https://doi.org/10.1016/j.biosystemseng.2017.02.002openalex-extracted
Weed segmentation using texture features extracted from wavelet sub-images
inferredcites →https://doi.org/10.1016/j.biosystemseng.2018.02.011openalex-extracted
A machine learning approach for pixel wise classification of residue and vegetation cover under field conditions
inferredcites →https://doi.org/10.1016/j.biosystemseng.2019.05.002openalex-extracted
Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN
inferredcites →https://doi.org/10.1016/j.biosystemseng.2020.11.005openalex-extracted
Bag-of-visual-words-augmented Histogram of Oriented Gradients for efficient weed detection
inferredcites →https://doi.org/10.1016/j.chemolab.2004.02.005openalex-extracted
Image texture analysis: methods and comparisons
inferredcites →https://doi.org/10.1016/j.compag.2007.05.008openalex-extracted
Autonomous robotic weed control systems: A review
inferredcites →https://doi.org/10.1016/j.compag.2007.12.007openalex-extracted
Selection of the most efficient wavelength bands for discriminating weeds from crop
inferredcites →https://doi.org/10.1016/j.compag.2008.08.002openalex-extracted
Mean-shift-based color segmentation of images containing green vegetation
inferredcites →https://doi.org/10.1016/j.compag.2008.09.001openalex-extracted
Improving weed pressure assessment using digital images from an experience-based reasoning approach
inferredcites →https://doi.org/10.1016/j.compag.2010.09.013openalex-extracted
Automatic segmentation of relevant textures in agricultural images
inferredcites →https://doi.org/10.1016/j.compag.2011.10.018openalex-extracted
Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine
inferredcites →https://doi.org/10.1016/j.compag.2012.05.015openalex-extracted
Aquatic weed automatic classification using machine learning techniques
inferredcites →https://doi.org/10.1016/j.compag.2013.04.010openalex-extracted
Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model
inferredcites →https://doi.org/10.1016/j.compag.2015.08.023openalex-extracted
Exploiting affine invariant regions and leaf edge shapes for weed detection
inferredcites →https://doi.org/10.1016/j.compag.2015.12.016openalex-extracted
Weed detection using image processing under different illumination for site-specific areas spraying
inferredcites →https://doi.org/10.1016/j.compag.2016.04.024openalex-extracted
A survey of image processing techniques for plant extraction and segmentation in the field
inferredcites →https://doi.org/10.1016/j.compag.2016.11.021openalex-extracted
Automatic crop detection under field conditions using the HSV colour space and morphological operations
inferredcites →https://doi.org/10.1016/j.compag.2017.01.001openalex-extracted
Weed identification based on K-means feature learning combined with convolutional neural network
inferredcites →https://doi.org/10.1016/j.compag.2017.01.008openalex-extracted
Weed detecting robot in sugarcane fields using fuzzy real time classifier
inferredcites →https://doi.org/10.1016/j.compag.2017.05.018openalex-extracted
Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland
inferredcites →https://doi.org/10.1016/j.compag.2017.10.027openalex-extracted
Weed detection in soybean crops using ConvNets
inferredcites →https://doi.org/10.1016/j.compag.2017.12.032openalex-extracted
Evaluation of support vector machine and artificial neural networks in weed detection using shape features
inferredcites →https://doi.org/10.1016/j.compag.2018.08.032openalex-extracted
Improving efficiency of organic farming by using a deep learning classification approach
inferredcites →https://doi.org/10.1016/j.compag.2018.09.021openalex-extracted
AgroAVNET for crops and weeds classification: A step forward in automatic farming
inferredcites →https://doi.org/10.1016/j.compag.2019.02.005openalex-extracted
A review on weed detection using ground-based machine vision and image processing techniques
inferredcites →https://doi.org/10.1016/j.compag.2019.104973openalex-extracted
Deep learning-based visual recognition of rumex for robotic precision farming
inferredcites →https://doi.org/10.1016/j.compag.2020.105306openalex-extracted
Towards weeds identification assistance through transfer learning
inferredcites →https://doi.org/10.1016/j.compag.2020.105450openalex-extracted
CNN feature based graph convolutional network for weed and crop recognition in smart farming
inferredcites →https://doi.org/10.1016/j.compag.2020.105520openalex-extracted
Graph weeds net: A graph-based deep learning method for weed recognition
inferredcites →https://doi.org/10.1016/j.compag.2020.105590openalex-extracted
Weed density classification in rice crop using computer vision
inferredcites →https://doi.org/10.1016/j.compag.2020.105750openalex-extracted
A DNN-based semantic segmentation for detecting weed and crop
inferredcites →https://doi.org/10.1016/j.compag.2020.105931openalex-extracted
Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks
inferredcites →https://doi.org/10.1016/j.compag.2021.106621openalex-extracted
Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery
inferredcites →https://doi.org/10.1016/j.compag.2022.107284openalex-extracted
Segmentation algorithm for overlap recognition of seedling lettuce and weeds based on SVM and image blocking
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Corn Seedling Recognition Algorithm Based on Hyperspectral Image and Lightweight-3d-Cnn
inferredcites →https://doi.org/10.1016/j.compag.2023.107698openalex-extracted
Applications of deep learning in precision weed management: A review
inferredcites →https://doi.org/10.1016/j.compind.2018.02.005openalex-extracted
Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems
inferredcites →https://doi.org/10.1016/j.compind.2018.03.001openalex-extracted
A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms
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Classification of crops and weeds from digital images: A support vector machine approach
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Multi-year weed community dynamics and rice yields as influenced by tillage, crop establishment, and weed control: Implications for rice-maize rotations in the eastern Gangetic plains
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Object-level benchmark for deep learning-based detection and classification of weed species
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Deep learning for image-based weed detection in turfgrass
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Support Vector Machines for crop/weeds identification in maize fields
inferredcites →https://doi.org/10.1016/j.eswa.2014.11.011openalex-extracted
Feature decision-making ant colony optimization system for an automated recognition of plant species
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PlantNet: A dual-function point cloud segmentation network for multiple plant species
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Fruits and vegetables quality evaluation using computer vision: A review
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ApLeaf: An efficient android-based plant leaf identification system
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Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant
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Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming
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Machine Learning for High-Throughput Stress Phenotyping in Plants
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Color image segmentation: advances and prospects
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Brief review of invariant texture analysis methods
inferredcites →https://doi.org/10.1016/s0034-4257(97)00045-xopenalex-extracted
Opportunities and limitations for image-based remote sensing in precision crop management
inferredcites →https://doi.org/10.1017/s2040470017000206openalex-extracted
RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
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Electrochemically Assisted Remediation of Pesticides in Soils and Water: A Review
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DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
inferredcites →https://doi.org/10.1071/cp21626openalex-extracted
Weed recognition using deep learning techniques on class-imbalanced imagery
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Field spectroscopy for weed detection in wheat and chickpea fields
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The evolution of LiDAR and its application in high precision measurement
inferredcites →https://doi.org/10.1088/1757-899x/943/1/012001openalex-extracted
Weed Pixel Level Classification Based on Evolving Feature Selection on Local Binary Pattern With Shallow Network Classifier
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Mechatronic systems, communication, and control in precision agriculture
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Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation
inferredcites →https://doi.org/10.1109/access.2021.3056577openalex-extracted
Identification of Tobacco Crop Based on Machine Learning for a Precision Agricultural Sprayer
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Towards Classification of Weeds through Digital Image
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Low-Cost Weed Identification System Using Drones
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inferredcites →https://doi.org/10.1109/dasa53625.2021.9681930openalex-extracted
Weed Classification Based on Statistical Features from Gabor Transform Magnitude
inferredcites →https://doi.org/10.1109/elecom49001.2020.9296994openalex-extracted
Performance of ANN and AlexNet for weed detection using UAV-based images
inferredcites →https://doi.org/10.1109/icesc48915.2020.9155701openalex-extracted
Weed Seedling Detection Using Mask Regional Convolutional Neural Network
inferredcites →https://doi.org/10.1109/icivc.2018.8492831openalex-extracted
Broad-Leaf Weed Detection in Pasture
inferredcites →https://doi.org/10.1109/icra.2016.7487720openalex-extracted
An effective classification system for separating sugar beets and weeds for precision farming applications
inferredcites →https://doi.org/10.1109/icra.2017.7989612openalex-extracted
Towards unsupervised weed scouting for agricultural robotics
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Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming
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Automatic model based dataset generation for fast and accurate crop and weeds detection
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Research on Semantic Segmentation of High-resolution Remote Sensing Image Based on Full Convolutional Neural Network
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A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
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Densely Based Multi-Scale and Multi-Modal Fully Convolutional Networks for High-Resolution Remote-Sensing Image Semantic Segmentation
inferredcites →https://doi.org/10.1109/lagirs48042.2020.9165562openalex-extracted
Deep Convolutional Neural Networks for Weed Detection in Agricultural Crops Using Optical Aerial Images
inferredcites →https://doi.org/10.1109/lra.2017.2667039openalex-extracted
Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics
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weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
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Visual pattern recognition by moment invariants
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Textural Features for Image Classification
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A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1109/wacv.2014.6835733openalex-extracted
Plant classification system for crop /weed discrimination without segmentation
inferredcites →https://doi.org/10.1111/gfs.12583openalex-extracted
Drought stress impact on the performance of deep convolutional neural networks for weed detection in Bahiagrass
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Weed detection for site‐specific weed management: mapping and real‐time approaches
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Future farms without farmers
inferredcites →https://doi.org/10.1145/2987386.2987433openalex-extracted
Development of Enhanced Weed Detection System with Adaptive Thresholding and Support Vector Machine
inferredcites →https://doi.org/10.11591/ijece.v6i3.9978openalex-extracted
A Fast and Efficient Shape Descriptor for an Advanced Weed Type Classification Approach
inferredcites →https://doi.org/10.1177/0278364917720510openalex-extracted
Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields
inferredcites →https://doi.org/10.1186/s13007-022-00869-zopenalex-extracted
A hybrid CNN–SVM classifier for weed recognition in winter rape field
inferredcites →https://doi.org/10.13031/2013.2970openalex-extracted
COLOR IMAGE SEGMENTATION WITH GENETIC ALGORITHM FOR IN-FIELD WEED SENSING
inferredcites →https://doi.org/10.13031/2013.29734openalex-extracted
Adaboost and Support Vector Machine Classifiers for Automatic Weed Control: Canola and Wheat
inferredcites →https://doi.org/10.13031/aim.20152188868openalex-extracted
Performance and Evaluation of Intra-Row Weeder Ultrasonic Plant Detection System and Pinch-Roller Weeding Mechanism for Vegetable Crops
inferredcites →https://doi.org/10.1371/journal.pone.0215676openalex-extracted
Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields
inferredcites →https://doi.org/10.1371/journal.pone.0249436openalex-extracted
Real-time recognition of spraying area for UAV sprayers using a deep learning approach
inferredcites →https://doi.org/10.14264/uql.2016.821openalex-extracted
Site-Specific Weed Management Using Remote Sensing
inferredcites →https://doi.org/10.15388/22-infor498openalex-extracted
Multi-Spectral Imaging for Weed Identification in Herbicides Testing
inferredcites →https://doi.org/10.15406/apar.2018.08.00333openalex-extracted
Ecologically sustainable integrated weed management in dry and irrigated direct-seeded rice
inferredcites →https://doi.org/10.18502/keg.v5i6.7046openalex-extracted
Automated Weed Detection Systems: A Review
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The Impact of Organic Farming on the Rural Economy in England
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Ground-based hyperspectral remote sensing for weed management in crop production
inferredcites →https://doi.org/10.25165/j.ijabe.20171005.2264openalex-extracted
Distance-based separability criterion of ROI in classification of farmland hyper-spectral images
inferredcites →https://doi.org/10.32604/csse.2022.023016openalex-extracted
CNN Based Automated Weed Detection System Using UAV Imagery
inferredcites →https://doi.org/10.3389/feart.2023.1146558openalex-extracted
Identification of maize and wheat seedlings and weeds based on deep learning
inferredcites →https://doi.org/10.3389/fpls.2019.01422openalex-extracted
Weed Detection in Perennial Ryegrass With Deep Learning Convolutional Neural Network
inferredcites →https://doi.org/10.3389/fpls.2022.1017803openalex-extracted
YOLOX-based blue laser weeding robot in corn field
inferredcites →https://doi.org/10.3390/agriengineering2030032openalex-extracted
A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images
inferredcites →https://doi.org/10.3390/agronomy13071780openalex-extracted
Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications
inferredcites →https://doi.org/10.3390/app12031308openalex-extracted
TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots
inferredcites →https://doi.org/10.3390/rs10111690openalex-extracted
Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images
inferredcites →https://doi.org/10.3390/rs12081246openalex-extracted
Open Plant Phenotype Database of Common Weeds in Denmark
inferredcites →https://doi.org/10.3390/rs12244185openalex-extracted
Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks
inferredcites →https://doi.org/10.3390/rs13010123openalex-extracted
Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology
inferredcites →https://doi.org/10.3390/rs13224606openalex-extracted
UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
inferredcites →https://doi.org/10.3390/rs14030559openalex-extracted
A Review of Deep Learning in Multiscale Agricultural Sensing
inferredcites →https://doi.org/10.3390/rs14215388openalex-extracted
Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image
inferredcites →https://doi.org/10.3390/s110606270openalex-extracted
Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination
inferredcites →https://doi.org/10.3390/s140815304openalex-extracted
A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method
inferredcites →https://doi.org/10.3390/s18041253openalex-extracted
A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms
inferredcites →https://doi.org/10.3390/s18072113openalex-extracted
A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery
inferredcites →https://doi.org/10.3390/s18103299openalex-extracted
Accurate Weed Mapping and Prescription Map Generation Based on Fully Convolutional Networks Using UAV Imagery
inferredcites →https://doi.org/10.3390/s19051058openalex-extracted
CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture
inferredcites →https://doi.org/10.3390/s19173787openalex-extracted
A System for Weeds and Crops Identification—Reaching over 10 FPS on Raspberry Pi with the Usage of MobileNets, DenseNet and Custom Modifications
inferredcites →https://doi.org/10.3390/s20082193openalex-extracted
Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies
inferredcites →https://doi.org/10.3390/s21010212openalex-extracted
Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
inferredcites →https://doi.org/10.3390/s21113647openalex-extracted
Review of Weed Detection Methods Based on Computer Vision
inferredcites →https://doi.org/10.3390/s22083021openalex-extracted
Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning
inferredcites →https://doi.org/10.3390/s22249723openalex-extracted
Evaluation of a CNN-Based Modular Precision Sprayer in Broadcast-Seeded Field
inferredcites →https://doi.org/10.3390/s23010062openalex-extracted
Deep Learning in Diverse Intelligent Sensor Based Systems
inferredcites →https://doi.org/10.3390/smartcities3030039openalex-extracted
Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review
inferredcites →https://doi.org/10.3844/ajabssp.2014.174.193openalex-extracted
DEVELOPMENT OF A MACHINE VISION SYSTEM FOR WEED DETECTION DURING BOTH OF OFF-SEASON AND IN-SEASON IN BROADACRE NO-TILLAGE CROPPING LANDS
inferredcites →https://doi.org/10.48550/arxiv.1711.05458openalex-extracted
A Public Image Database for Benchmark of Plant Seedling Classification Algorithms
inferredcites →https://doi.org/10.5565/rev/elcvia.826openalex-extracted
A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation
inferredcites →https://doi.org/10.6688/jise.2014.30.4.17openalex-extracted
Weed Image Classification using Wavelet Transform, Stepwise Linear Discriminant Analysis, and Support Vector Machines for an Automatic Spray Control System *
inferredcites →https://doi.org/10.11591/ijece.v6i3.pp1168-1175openalex-snapshot
A Fast and Efficient Shape Descriptor for an Advanced Weed Type Classification Approach
inferredcites →https://doi.org/10.25165/ijabe.v10i5.2264openalex-snapshot
Distance-based separability criterion of ROI in classification of farmland hyper-spectral images
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