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IEEE DOI
2410
Precision agriculture, Solid modeling, Fourier transforms, Fluctuations,
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IEEE DOI
2404
Training, Deep learning, Invasive species, Costs, Plankton,
Machine vision, Supervised learning
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ICCVMI23(1-6)
IEEE DOI
2403
Analytical models, Uncertainty, Computational modeling,
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Weeds Classification with Deep Learning: An Investigation Using Cnn,
Vision Transformers, Pyramid Vision Transformers, and Ensemble Strategy,
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Springer DOI
2312
BibRef
Steininger, D.[Daniel],
Trondl, A.[Andreas],
Croonen, G.[Gerardus],
Simon, J.[Julia],
Widhalm, V.[Verena],
The CropAndWeed Dataset: a Multi-Modal Learning Approach for
Efficient Crop and Weed Manipulation,
WACV23(3718-3727)
IEEE DOI
2302
Location awareness, Training, Image segmentation, Annotations, Crops,
Training data, Benchmark testing, Applications: Agriculture, visual reasoning
BibRef
Iancu, O.D.[Ovidiu Dan],
Yang, K.[Kara],
Man, H.[Han],
Menard, T.C.[Theresa Cabrera],
An Automated and Scalable ML Solution for Mapping Invasive Species:
the Case of the Australian Tree Fern in Hawaiian Forests,
RealWorld23(140-147)
IEEE DOI
2302
Training, Web services, Pipelines, Training data, Vegetation, Organizations
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Elias, N.[Nathan],
Deep Learning Methodology for Early Detection and Outbreak Prediction
of Invasive Species Growth,
WACV23(6324-6332)
IEEE DOI
2302
Training, Deep learning, Solid modeling, Laser radar, Manuals,
Predictive models, Animals/Insects
BibRef
Amziane, A.[Anis],
Losson, O.[Olivier],
Mathon, B.[Benjamin],
Dumenil, A.[Aurélien],
Macaire, L.[Ludovic],
Frame-based reflectance estimation from multispectral images for weed
identification in varying illumination conditions,
IPTA20(1-7)
IEEE DOI
2206
Reflectivity, Image segmentation, Lighting, Estimation,
Vegetation mapping, Tools, Cameras, Multispectral imaging, Linescan camera
BibRef
Gimenez, R.,
Lassalle, G.,
Hédacq, R.,
Elger, A.,
Dubucq, D.,
Credoz, A.,
Jennet, C.,
Fabre, S.,
Exploitation of Spectral and Temporal Information for Mapping Plant
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ISPRS21(B3-2021: 559-566).
DOI Link
2201
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Liu, J.,
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Chen, D.,
A Procedure for Identifying Invasive Wild Parsnip Plants Based On
Visible Bands From UAV Images,
ISPRS21(B1-2021: 173-181).
DOI Link
2201
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Nyawacha, S.O.,
Meta, V.,
Osio, A.,
Spatial Temporal Mapping of Spread of Water Hyacinth In Winum Gulf,
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ISPRS21(B3-2021: 341-346).
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2201
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Asad, M.H.[Muhammad Hamza],
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Weed Density Estimation Using Semantic Segmentation,
PSIVT19(162-171).
Springer DOI
2003
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Baidar, T.,
Shrestha, A.B.,
Ranjit, R.,
Adhikari, R.,
Ghimire, S.,
Shrestha, N.,
Impact Assessment of Mikania Micrantha On Land Cover And Maxent
Modeling to Predict Its Potential Invasion Sites,
Hannover17(305-310).
DOI Link
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Martínez-Sánchez, J.,
González-de Santos, L.M.,
Novo, A.,
González-Jorge, H.,
UAV and Satellite Imagery Applied to Alien Species Mapping in NW Spain,
UAV-g19(455-459).
DOI Link
1912
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Mudereri, B.T.,
Dube, T.,
Adel-Rahman, E.M.,
Niassy, S.,
Kimathi, E.,
Khan, Z.,
Landmann, T.,
A Comparative Analysis of Planetscope and Sentinel Sentinel-2
Space-borne Sensors in Mapping Striga Weed Using Guided Regularised
Random Forest Classification Ensemble,
IWIDF19(701-708).
DOI Link
1912
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Förster, M.,
Schmidt, T.,
Wolf, R.,
Kleinschmit, B.,
Fassnacht, F.E.,
Cabezas, J.,
Kattenborn, T.,
Detecting the spread of invasive species in central Chile with a
Sentinel-2 time-series,
MultiTemp17(1-4)
IEEE DOI
1712
geophysical image processing, hyperspectral imaging,
image segmentation, land cover, least squares approximations,
time-series
BibRef
Marshall, V.,
Lewis, M.,
Ostendorf, B.,
Do Additional Bands (coastal, Nir-2, Red-edge And Yellow) In
Worldview-2 Multispectral Imagery Improve Discrimination Of An Invasive
Tussock, Buffel Grass (cenchrus Ciliaris)?,
ISPRS12(XXXIX-B8:277-281).
DOI Link
1209
P>
Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Soybean Crop Analysis, Beans, Production, Detection, Health, Change .