A review on deep learning in UAV remote sensing LP Osco, JM Junior, APM Ramos, LA de Castro Jorge, SN Fatholahi, ... International Journal of Applied Earth Observation and Geoinformation 102 …, 2021 | 414 | 2021 |
Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs AA Santos, J Marcato Junior, MS Araújo, DR Di Martini, EC Tetila, ... Sensors 19 (16), 3595, 2019 | 178 | 2019 |
BERT for stock market sentiment analysis MG Sousa, K Sakiyama, L de Souza Rodrigues, PH Moraes, ... 2019 IEEE 31st international conference on tools with artificial …, 2019 | 176 | 2019 |
A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery LP Osco, MS De Arruda, JM Junior, NB Da Silva, APM Ramos, ... ISPRS Journal of Photogrammetry and Remote Sensing 160, 97-106, 2020 | 169 | 2020 |
Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-imagery L Prado Osco, AP Marques Ramos, D Roberto Pereira, ... Remote Sensing 11 (24), 2925, 2019 | 136 | 2019 |
Deep learning applied to phenotyping of biomass in forages with UAV-based RGB imagery W Castro, J Marcato Junior, C Polidoro, LP Osco, W Gonçalves, ... Sensors 20 (17), 4802, 2020 | 79 | 2020 |
Pretext: Uma ferramenta para pré-processamento de textos utilizando a abordagem bag-of-words ET Matsubara, CA Martins, MC Monard | 69 | 2003 |
A simple lexicographic ranker and probability estimator P Flach, ET Matsubara European Conference on Machine Learning, 575-582, 2007 | 64 | 2007 |
Cattle weight estimation using active contour models and regression trees Bagging VAM Weber, F de Lima Weber, A da Silva Oliveira, G Astolfi, GV Menezes, ... Computers and electronics in agriculture 179, 105804, 2020 | 63 | 2020 |
Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks F de Lima Weber, VA de Moraes Weber, GV Menezes, ASO Junior, ... Computers and Electronics in Agriculture 175, 105548, 2020 | 55 | 2020 |
Estimating pasture biomass and canopy height in Brazilian savanna using UAV photogrammetry J Batistoti, J Marcato Junior, L Ítavo, E Matsubara, E Gomes, B Oliveira, ... Remote Sensing 11 (20), 2447, 2019 | 53 | 2019 |
Reducing the dimensionality of bag-of-words text representation used by learning algorithms CA Martins, MC Monard, ET Matsubara Proc of 3rd IASTED International Conference on Artificial Intelligence and …, 2003 | 51 | 2003 |
POLLEN73S: an image dataset for pollen grains classification G Astolfi, AB Goncalves, GV Menezes, FSB Borges, ACMN Astolfi, ... Ecological Informatics 60, 101165, 2020 | 50 | 2020 |
Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images LP Osco, APM Ramos, ÉAS Moriya, M de Souza, JM Junior, ... International Journal of Applied Earth Observation and Geoinformation 83, 101907, 2019 | 40 | 2019 |
Benchmarking anchor-based and anchor-free state-of-the-art deep learning methods for individual tree detection in rgb high-resolution images P Zamboni, JM Junior, JA Silva, GT Miyoshi, ET Matsubara, K Nogueira, ... Remote Sensing 13 (13), 2482, 2021 | 35 | 2021 |
Counting and locating high-density objects using convolutional neural network MS de Arruda, LP Osco, PR Acosta, DN Gonçalves, JM Junior, ... Expert Systems with Applications 195, 116555, 2022 | 28 | 2022 |
Convolutional neural networks to estimate dry matter yield in a Guineagrass breeding program using UAV remote sensing GS de Oliveira, J Marcato Junior, C Polidoro, LP Osco, H Siqueira, ... Sensors 21 (12), 3971, 2021 | 26 | 2021 |
Counting cattle in UAV images using convolutional neural network F de Lima Weber, VA de Moraes Weber, PH de Moraes, ET Matsubara, ... Remote Sensing Applications: Society and Environment 29, 100900, 2023 | 25 | 2023 |
Multi-view Semi-supervised Learning: An Approach to Obtain Different Views from Text Datasets. ET Matsubara, MC Monard, GE Batista LAPTEC, 97-104, 2005 | 25 | 2005 |
Combining unigrams and bigrams in semi-supervised text classification I Braga, M Monard, E Matsubara Proceedings of progress in artificial intelligence, 14th Portuguese …, 2009 | 24 | 2009 |