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Predicting coffee water potential from spectral reflectance indices with neural networks. Author(s): ALVES, H. M. R. Leaf water potential is one of the main parameters used to assess water relations in plants by revealing levels of tissue hydration. It is commonly measured with the Scholander pressure chamber; which... ... Repository: BDPA Publication year: 2023
Classification of apple tree disorders using Convolutional Neural Networks. Author(s): 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
Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping. Author(s): CHAGAS, C. da S. Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromise... ... Repository: BDPA Publication year: 2013
Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. Author(s): CAIXETA, E. T. Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (AN... ... Repository: BDPA Publication year: 2022
Artificial neural networks in the classification and identification of soybean cultivars by planting region. Author(s): GALÃO, O. F.; BORSATO, D.; PINTO, J. P.; VISENTAINER, J. V.; CARRÃO-PANIZZI, M. C. Vinte variedades de soja (Glycine max), quatorze convencionais e seis variedades transgênicas (RR) foram analisadas quanto ao teor de proteína, ácido fítico, teor de óleo, fitosteróis, cinzas, minerai... ... Repository: BDPA Publication year: 2011
Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks. Author(s): SANO, E. E. Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorit... ... Repository: BDPA Publication year: 2022
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes. Author(s): NASCIMENTO, M.; PETERNELLI, L. A.; CRUZ, C. D.; NASCIMENTO, A. C. C.; FERREIRA, R. de P. The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology o... ... Repository: BDPA Publication year: 2013
Digital soilscape mapping of tropical hillslope areas by neural networks. Author(s): CARVALHO JUNIOR, W. de; CHAGAS, C. da S.; BHERING, S. B. Geomorphometric variables are applied in digital soil mapping because of their strong correlation with the disposition and distribution of pedological components of the landscapes. In this research, t... ... Repository: BDPA Publication year: 2011
Digital soil mapping using reference area and artificial neural networks. Author(s): CHAGAS, C. da S. Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artif... ... Repository: BDPA Publication year: 2016
Artificial neural networks classify cotton genotypes for fiber length. Author(s): CARVALHO, L. P. de; TEODORO, P. E.; BARROSO, L. M. A.; FARIAS, F. J. C.; MORELLO, C. de L.; NASCIMENTO, M. Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater... ... Repository: BDPA Publication year: 2018

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