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Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

https://doi.org/10.1109/access.2021.3057912
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What this work says

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

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Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming
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A survey of image processing techniques for plant extraction and segmentation in the field
inferredcites →https://doi.org/10.1016/j.compag.2017.01.001openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compag.2017.04.011openalex-snapshot
An adaptive approach for UAV-based pesticide spraying in dynamic environments
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A review on weed detection using ground-based machine vision and image processing techniques
inferredcites →https://doi.org/10.1016/j.compag.2019.05.036openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compag.2019.104963openalex-snapshot
Unsupervised deep learning and semi-automatic data labeling in weed discrimination
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Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation
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A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms
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inferredcites →https://doi.org/10.1017/s2040470017000206openalex-snapshot
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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Random Forests
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DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
inferredcites →https://doi.org/10.1080/02772249909358810openalex-snapshot
Effect of agrochemicals on earthworms
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LIII. On lines and planes of closest fit to systems of points in space
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Support vector machines
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Fetal Weight Estimation via Ultrasound Using Machine Learning
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ImageNet: A large-scale hierarchical image database
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inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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Unsupervised Image Segmentation by Backpropagation
inferredcites →https://doi.org/10.1109/icra.2017.7989612openalex-snapshot
Towards unsupervised weed scouting for agricultural robotics
inferredcites →https://doi.org/10.1109/icra.2018.8460962openalex-snapshot
Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
inferredcites →https://doi.org/10.1109/irc.2019.00029openalex-snapshot
Crop and Weeds Classification for Precision Agriculture Using Context-Independent Pixel-Wise Segmentation
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/lra.2017.2774979openalex-snapshot
weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
inferredcites →https://doi.org/10.1109/lra.2018.2846289openalex-snapshot
Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming
inferredcites →https://doi.org/10.1109/tpami.2012.120openalex-snapshot
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1126/science.1183899openalex-snapshot
Precision Agriculture and Food Security
inferredcites →https://doi.org/10.1177/0278364917720510openalex-snapshot
Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields
inferredcites →https://doi.org/10.1186/s13640-015-0087-0openalex-snapshot
Segmentation and size estimation of tomatoes from sequences of paired images
inferredcites →https://doi.org/10.1590/s0100-83582015000100018openalex-snapshot
Weed mapping using techniques of precision agriculture
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.3390/agronomy10010113openalex-snapshot
Weed Density Detection Method Based on Absolute Feature Corner Points in Field
inferredcites →https://doi.org/10.3390/jimaging3010009openalex-snapshot
3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions
inferredcites →https://doi.org/10.3390/rs10091423openalex-snapshot
WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming
inferredcites →https://doi.org/10.3390/rs11030249openalex-snapshot
Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density
inferredcites →https://doi.org/10.3390/rs61212037openalex-snapshot
Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV
inferredcites →https://doi.org/10.3390/s131114662openalex-snapshot
Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor
inferredcites →https://doi.org/10.3390/s18041253openalex-snapshot
A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms
inferredcites →https://doi.org/10.3390/s18124245openalex-snapshot
A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
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.1411.1792openalex-snapshot
How transferable are features in deep neural networks?
inferredcites →https://doi.org/10.48550/arxiv.1803.08375openalex-snapshot
Deep Learning using Rectified Linear Units (ReLU)
inferredcites →https://doi.org/10.5220/0004818804020411openalex-snapshot
Shape-based Segmentation of Tomatoes for Agriculture Monitoring
inferredcites →https://doi.org/10.5860/choice.33-1577openalex-snapshot
Artificial intelligence: a modern approach
inferredcites →https://doi.org/10.5958/2230-732x.2017.00083.3openalex-snapshot
Effects of Chemical Fertilizers and Pesticides on Human Health and Environment: A Review
inferredcites →https://doi.org/10.7551/mitpress/7503.003.0147openalex-snapshot
Efficient Learning of Sparse Representations with an Energy-Based Model

What points at this work

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

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Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
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Multi-UAV Weed Spraying
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Cultivating with Precision: Mitigating Risks and Maximizing Yields
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Towards Resilient Agriculture: A Novel UAV-Based Lightweight Deep Learning Framework for Wheat Head Detection
inferred← citeshttps://doi.org/10.3390/ndt3020010openalex-snapshot
Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling
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/rs15194687openalex-snapshot
Supervised Geomorphic Mapping of Himalayan Rivers Based on Sentinel-2 Data
inferred← citeshttps://doi.org/10.3390/rs17162796openalex-snapshot
Adaptive Clustering-Guided Multi-Scale Integration for Traffic Density Estimation in Remote Sensing Images
inferred← citeshttps://doi.org/10.3390/s22083043openalex-snapshot
Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions
inferred← citeshttps://doi.org/10.36306/konjes.1097969openalex-snapshot
COMPUTER VISION IN PRECISION AGRICULTURE FOR WEED CONTROL: A SYSTEMATIC LITERATURE REVIEW
inferred← citeshttps://doi.org/10.36306/konjes.1583103openalex-snapshot
DEEP LEARNING APPROACHES IN PRECISION AGRICULTURE: A COMPREHENSIVE REVIEW OF CROP CLASSIFICATION, DISEASE DETECTION, AND WEED DETECTION TECHNIQUES
inferred← citeshttps://doi.org/10.47836/pjst.33.s3.02openalex-snapshot
Weeds Detection for Agriculture Using Convolutional Neural Network (CNN) Algorithm for Sustainable Productivity
inferred← citeshttps://doi.org/10.48175/ijarsct-2301openalex-snapshot
A Comprehensive Overview of Machine Learning Algorithms and their Applications
inferred← citeshttps://doi.org/10.51694/advweedsci/2024;42:00004openalex-snapshot
Machine learning algorithms applied to weed management in integrated crop-livestock systems: a systematic literature review
inferred← citeshttps://doi.org/10.52783/jes.1476openalex-snapshot
Devising of an Efficient Multi-parametric System for Prediction of Crop Yield using Augmented Incremental Machine & Deep Learning Operations
inferred← citeshttps://doi.org/10.53555/sfs.v10i4s.1294openalex-snapshot
https://sifisheriessciences.com/journal/index.php/journal/article/view/1294
inferred← citeshttps://doi.org/10.56369/tsaes.5381openalex-snapshot
THE INTERACTION BETWEEN WEED AND ARTHROPODS OF AGROFORESTRY ECOSYSTEMS IN CENTRAL JAVA, INDONESIA
inferred← citeshttps://doi.org/10.59879/ty0kzopenalex-snapshot
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inferred← citeshttps://doi.org/10.7759/s44389-025-06385-0openalex-snapshot
An Efficient Deep Learning Approach for Accurate Quality Assessment of Plant-Based Foods in Sustainable Agriculture
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