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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
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
A study on the detection of cattle in UAV images using deep learning. Author(s): BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, T. T.; SANTOS, P. M. Abstract: Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive... ... Repository: BDPA Publication year: 2019
Counting cattle in UAV images: dealing with clustered animals and animal/background contrast changes. Author(s): BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M.; RIBEIRO, A. R. B. Abstract: The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest i... ... Repository: BDPA Publication year: 2020

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