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Classification of apple tree disorders using Convolutional Neural Networks. Author(s): NACHTIGALL, L. G.; ARAUJO, R. M.; NACHTIGALL, G. R. Abstract?This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of thei... ... Repository: BDPA Publication year: 2016
Fusing multi-season UAS images with convolutional neural networks to map tree species in Amazonian forests. Author(s): VERAS, H. F. P.; FERREIRA, M. P.; CUNHA NETO, E. M. da; FIGUEIREDO, E. O.; DALLA CORTE, A. P.; SANQUETTA, C. R. Remote sensing images obtained by unoccupied aircraft systems (UAS) across different seasons enabled capturing of species-specific phenological patterns of tropical trees. The application of UAS multi... ... Repository: BDPA Publication year: 2022
Classification of weeds in agricultural crops with ensembles of convolutional neural networks. Author(s): CARVALHO, V. M. de S.; GUEDES, E. B.; SALAME, M. F. A. Classificação de Ervas Daninhas em Culturas Agrícolas com Comitês de Redes Neurais Convolucionais. Repository: BDPA Publication year: 2020
Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing. Author(s): OLIVEIRA, G. S. de; MARCATO JUNIOR, J.; POLIDORO, C.; OSCO, L. P.; SIQUEIRA, H.; RODRIGUES, L.; JANK, L.; BARRIOS, S. C. L.; VALLE, C.; SIMEÃO, R. M.; CARROMEU, C.; SILVEIRA, E.; JORGE, L. A. de C.; GONÇALVES, W.; SANTOS, M. F.; MATSUBARA, E. Repository: BDPA Publication year: 2021
Sistema de coleta de imagens para previsão de produção em pomares de maçã. Author(s): SELZLEIN, D. A.; RIBEIRO, C. M. A. A.; GEBLER, L.; PAULA FILHO, P. L. DE A coleta de imagens é uma importante ferramenta para a agricultura de precisão. Porém, certas culturas, como a da maçã, dificultam sua realização devido ao uso de telas de proteção antigranizo, o que... ... Repository: BDPA Publication year: 2024
A methodology for detection and localization of fruits in apples orchards from aerial images. Author(s): SANTOS, T. T.; GEBLER, L. Abstract. Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integr... ... Repository: BDPA Publication year: 2021
InsectCV: a system for insect detection in the lab from trap images. Author(s): CESARO JÚNIOR, T. de; RIEDER, R.; DI DOMÊNICO, J. R.; LAU, D. Advances in artificial intelligence, computer vision, and high-performance computing have enabled the creation of efficient solutions to monitor pests and identify plant diseases. In this context, we... ... Repository: BDPA Publication year: 2021
A study on CNN-based detection of psyllids in sticky traps using multiple image data sources. Author(s): BARBEDO, J. G. A.; CASTRO, G. B. Abstract: Deep learning architectures like Convolutional Neural Networks (CNNs) are quickly becoming the standard for detecting and counting objects in digital images. However, most of the experiments... ... Repository: BDPA Publication year: 2020
Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests. Author(s): GONÇALVES, J. P.; PINTO, F. A. C.; QUEIROZ, D. M.; VILLAR, F. M. M.; BARBEDO, J. G. A.; DEL PONTE, E. M. Colour-thresholding digital imaging methods are generally accurate for measuring the percentage of foliar area affected by disease or pests (severity), but they perform poorly when scene illumination... ... Repository: BDPA Publication year: 2021
Cattle detection using oblique UAV images. Author(s): BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M. The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The... ... Repository: BDPA Publication year: 2020

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