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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
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
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
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
Use of images of leaves and fruits of apple trees for automatic identification of symptoms of diseases and nutritional disorders. Author(s): NACHTIGALL, L. G.; ARAUJO, R. M.; NACHTIGALL, G. R. Rapid diagnosis ofsymptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficienc... ... Repository: BDPA Publication year: 2017
Accessing the dry matter yield persistence in an alfalfa germplasm within Brazil through random regression models. Author(s): SANTOS, I. G.; PEIXOTO, M. A.; CRUZ, C. D.; FERREIRA, R. de P.; NASCIMENTO, M. Persistence plays a key role in alfalfa cultivation in tropical areas but is still a bottleneck for breeding programs. Repository: BDPA Publication year: 2021

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