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Machine learning and handcrafted image processing methods for classifying common weeds in corn field

https://doi.org/10.1016/j.atech.2023.100249

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What this work says

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

inferredcites →https://doi.org/10.1007/978-981-15-5463-6openalex-snapshot

Advances in Electromechanical Technologies

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A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms

inferredcites →https://doi.org/10.1007/s43154-020-00001-wopenalex-snapshot

Weed Detection for Selective Spraying: a Review

inferredcites →https://doi.org/10.1016/c2015-0-04331-3openalex-snapshot

Fundamentals of Weed Science

inferredcites →https://doi.org/10.1016/j.biosystemseng.2005.04.011openalex-snapshot

Weed Classification by Active Shape Models

inferredcites →https://doi.org/10.1016/j.biosystemseng.2020.03.006openalex-snapshot

Determination of key canopy parameters for mass mechanical apple harvesting using supervised machine learning and principal component analysis (PCA)

inferredcites →https://doi.org/10.1016/j.compag.2016.04.024openalex-snapshot

A survey of image processing techniques for plant extraction and segmentation in the field

inferredcites →https://doi.org/10.1016/j.compag.2017.07.028openalex-snapshot

Maize and weed classification using color indices with support vector data description in outdoor fields

inferredcites →https://doi.org/10.1016/j.compag.2017.12.032openalex-snapshot

Evaluation of support vector machine and artificial neural networks in weed detection using shape features

inferredcites →https://doi.org/10.1016/j.compag.2019.02.005openalex-snapshot

A review on weed detection using ground-based machine vision and image processing techniques

inferredcites →https://doi.org/10.1016/j.compag.2020.105590openalex-snapshot

Weed density classification in rice crop using computer vision

inferredcites →https://doi.org/10.1016/j.compag.2021.106081openalex-snapshot

Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems

inferredcites →https://doi.org/10.1016/j.compag.2021.106348openalex-snapshot

Image based thermal sensing for glyphosate resistant weed identification in greenhouse conditions

inferredcites →https://doi.org/10.1016/j.compag.2022.107064openalex-snapshot

A review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops

inferredcites →https://doi.org/10.1016/j.compag.2023.107698openalex-snapshot

Applications of deep learning in precision weed management: A review

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Distinguishing seedling volunteer corn from soybean through greenhouse color, color-infrared, and fused images using machine and deep learning

inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.01.011openalex-snapshot

Random forest in remote sensing: A review of applications and future directions

inferredcites →https://doi.org/10.1016/j.jag.2017.12.012openalex-snapshot

Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery

inferredcites →https://doi.org/10.1017/wet.2016.2openalex-snapshot

Perspectives on Potential Soybean Yield Losses from Weeds in North America

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1038/nmeth.2089openalex-snapshot

NIH Image to ImageJ: 25 years of image analysis

inferredcites →https://doi.org/10.1038/s41598-023-33042-0openalex-snapshot

Towards reducing chemical usage for weed control in agriculture using UAS imagery analysis and computer vision techniques

inferredcites →https://doi.org/10.1109/iccsp37400.2016openalex-snapshot

2016 International Conference on Communication and Signal Processing (ICCSP)

inferredcites →https://doi.org/10.13031/2013.18144openalex-snapshot

SHAPE AND SIZE ANALYSIS OF CORN PLANT CANOPIES FOR PLANT POPULATION AND SPACING SENSING

inferredcites →https://doi.org/10.13031/2013.27838openalex-snapshot

Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions

inferredcites →https://doi.org/10.13031/trans.12105openalex-snapshot

Identification and Counting of Soybean Aphids from Digital Images Using Shape Classification

inferredcites →https://doi.org/10.15446/ing.investig.v37n1.54703openalex-snapshot

Weed recognition by SVM texture feature classification in outdoor vegetable crops images

inferredcites →https://doi.org/10.1614/wt-d-16-00046.1openalex-snapshot

Potential Corn Yield Losses from Weeds in North America

inferredcites →https://doi.org/10.17584/rcch.2021v15i2.11638openalex-snapshot

Use of multispectral images to evaluate the efficacy of pre-emergent herbicides in peas under greenhouse conditions

inferredcites →https://doi.org/10.3390/agriculture11050387openalex-snapshot

Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

inferredcites →https://doi.org/10.3390/agronomy10040466openalex-snapshot

Integrated Weed Management in Herbaceous Field Crops

inferredcites →https://doi.org/10.3390/plants9050559openalex-snapshot

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

inferredcites →https://doi.org/10.3390/rs10020285openalex-snapshot

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

inferredcites →https://doi.org/10.3390/rs10050761openalex-snapshot

Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information

inferredcites →https://doi.org/10.3390/rs12111838openalex-snapshot

Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms

inferredcites →https://doi.org/10.3390/rs12244143openalex-snapshot

Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

inferredcites →https://doi.org/10.3390/s18010018openalex-snapshot

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

inferredcites →https://doi.org/10.3390/sym9090179openalex-snapshot

NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier

inferredcites →https://doi.org/10.4186/ej.2017.21.2.81openalex-snapshot

A Comparative Analysis of Weed Images Classification Approaches in Vegetables Crops

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

What points at this work

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

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An Image Processing Based Approach to Assess the Quality of Pesticide Spray

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Integrated Machine Learning and Ontology-Based Approach for Sustainable Weed Management in Paddy Fields

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Use of hyperspectral sensor and random forest for broadleaf and narrowleaf weed differentiation

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Real-time early corn and weed classification using hybrid visual features based on parallel processing and tracking

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Key technologies in apple harvesting robot for standardized orchards: A comprehensive review of innovations, challenges, and future directions

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inferred← citeshttps://doi.org/10.1109/icic64337.2024.10956509openalex-snapshot

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