La Rosa et al. (2020)La Rosa LEC, Oliveira DAB, Zortea M, Gemignani BH, Feitosa RQ. 2020. Learning geometric features for improving the automatic detection of citrus plantation rows in UAV images. IEEE Geoscience and Remote Sensing Letters 19: 1-5. https://doi.org/10.1109/LGRS.2020.3024641 https://doi.org/10.1109/LGRS.2020.302464...
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Convolutional encoder-decoder network |
UAV-RGB |
Orthomosaic |
Plantation rows |
Citrus (orange) |
Di Gennaro and Matese (2020)Di Gennaro SF, Matese A. 2020. Evaluation of novel precision viticulture tool for canopy biomass estimation and missing plant detection based on 2.5D and 3D approaches using RGB images acquired by UAV platform. Plant Method 16: 1-12. https://doi.org/10.1186/s13007-020-00632-2 https://doi.org/10.1186/s13007-020-00632...
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2.5D-surface and 3D-alphashape methods (Edelsbrunner and Mücke, 1994Edelsbrunner H, Mücke EP. 1994. Three-dimensional alpha shapes. ACM Transactions on Graphics 13: 43-72. https://doi.org/10.1145/174462.156635 https://doi.org/10.1145/174462.156635...
) |
UAV-RGB |
Point clouds |
Biomass and missing plants |
Vine |
Fareed and Rehman (2020)Fareed N, Rehman K. 2020. Integration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model. ISPRS International Journal of Geo-Information 9: 151. https://doi.org/10.3390/ijgi9030151 https://doi.org/10.3390/ijgi9030151...
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DSM-based solutions, PCA component and K-means |
UAV-RGB |
Point clouds |
Plantation rows |
Eucalyptus |
Rabab et al. (2021) Rabab S , Badenhorst P , Chen YP , Daetwyler HD . 2021. A template-free machine vision-based crop row detection algorithm. Precision Agriculture 22: 124-153. https://doi.org/10.1007/s11119-020-09732-4 https://doi.org/10.1007/s11119-020-09732...
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Image segmentation with morphological Operations, triangular matrix and pre-defined parameters |
Terrestrial and handheld Camera RGB, Public data set (Vidović et al., 2016Vidovic I, Cupec R, Hocenski Ž. 2016. Crop row detection by global energy minimization. Pattern Recognition 55: 68-86. https://doi.org/10.1016/j.patcog.2016.01.013 https://doi.org/10.1016/j.patcog.2016.01...
) and UAV-RGB |
Images taken from the top and side views or Orthomosaic |
Crop row |
Maize, celery, potato, onion, sunflower and soya bean |
Chen et al. (2021) Chen P , Ma X , Wang F , Li J . 2021. A new method for crop row detection using unmanned aerial vehicle images. Remote Sensing 13: 3526. https://doi.org/10.3390/rs13173526 https://doi.org/10.3390/rs13173526...
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Image segmentation and Least squares fitting-based |
UAV-Multispectral |
Orthomosaic |
Row line |
Cotton and Wheat |
Biglia et al. (2022) Biglia A , Zaman S , Gay P , Aimonino DR , Comba L . 2022. 3D point cloud density-based segmentation for vine rows detection and localisation. Computers and Electronics in Agriculture 199: 107166. https://doi.org/10.1016/j.compag.2022.107166 https://doi.org/10.1016/j.compag.2022.10...
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A 3D point cloud processing algorithm based on the detection of key points and a density-based clustering approach |
UAV-Multispectral |
Point clouds |
Spatial location of each vine row |
Vine |
Rocha et al. (2023) Rocha BM , Fonseca AU , Pedrini H , Soares F . 2023. Automatic detection and evaluation of sugarcane planting rows in aerial images. Information Processing in Agriculture 10: 400-415. https://doi.org/10.1016/j.inpa.2022.04.003 https://doi.org/10.1016/j.inpa.2022.04.0...
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Image classification (decision tree, linear discriminant analysis and K-nearest neighbors), RGB gradient filter, morphological operations and computational geometry models |
UAV-RGB |
Orthomosaic |
Crop rows and gaps |
Sugarcane |
Ribeiro et al. (2023) Ribeiro JB , Silva RR , Dias JD , Escarpinati MC , Backes AR . 2023. Automated detection of sugarcane crop lines from UAV images using deep learning. Information Processing in Agriculture 10: 400-415. https://doi.org/10.1016/j.inpa.2023.04.001 https://doi.org/10.1016/j.inpa.2023.04.0...
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CNN (U-Net, LinkNet and PSPNet) and VGG16 network structure pre-trained with ImageNet |
UAV-RGB |
Orthomosaic |
Segmentation of crop lines |
Sugarcane |
Liu et al. (2023) Liu M , Su W , Wang X . 2023. Quantitative evaluation of maize emergence using UAV imagery and deep learning. Remote Sensing 15: 1979. https://doi.org/10.3390/rs15081979 https://doi.org/10.3390/rs15081979...
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Deep learning (YOLO V3) |
UAV-RGB |
Orthomosaic |
Seedling count, spacing and size |
Maize |