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Detecting aquaculture with deep learning in a low-data setting. Author(s): GREENSTREET, L.; FAN, J.; PACHECO, F. S.; BAI, Y.; UMMUS, M. E.; DORIA, C.; BARROS, N. O.; FORSBERG, B. R.; XU, X.; FLECKER, A.; GOMES, C. Aquaculture is growing rapidly in the Amazon basin and detailed spatial information is needed to understand the trade-offs between food production, economic development, and environmental impacts. Lar... ... Repository: BDPA Publication year: 2023
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
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
Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping. Author(s): CHAGAS, C. da S.; VIEIRA, C. A. O.; FERNANDES-FILHO, E. I. 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
Predicting coffee water potential from spectral reflectance indices with neural networks. Author(s): NUNES, P. H.; PIERANGELI, E. V.; SANTOS, M. O.; SILVEIRA, H. R. O.; MATOS, C. S. M. de; PEREIRA, A. B.; ALVES, H. M. R.; VOLPATO, M. M. L.; SILVA, V. A.; FERREIRA, D. D. 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
Estimativa da altura de árvores de eucalipto desbastado em sistema agrossilvipastoril com rede neural artificial. Author(s): MORAES NETO, S. P. de; PULROLNIK, K.; VILELA, L. Resumo: Uma abordagem alternativa para modelar relações hipsométricas envolve a aplicação de redes neurais artificiais (RNAs). O objetivo geral deste trabalho foi verificar a viabilidade da utilização... ... Repository: BDPA Publication year: 2024
Laser-induced breakdown spectroscopy as an analytical tool for total carbon quantification in tropical and subtropical soils: evaluation of calibration algorithms. Author(s): BABOS, D. V.; GUEDES, W. N.; FREITAS, V. S.; SILVA, F. P.; TOZO, M. L. L.; VILLAS-BOAS, P. R.; MARTIN NETO, L.; MILORI, D. M. B. P. The demand for efficient, accurate, and cost-effective methods of measuring soil carbon (C) in agriculture is growing. Traditional approaches are time consuming and expensive, highlighting the need fo... ... Repository: BDPA Publication year: 2024
Reconhecimento de bovino pantaneiro utilizando visão computacional através da rede neural convolucional: resultados preliminares. Author(s): WEBER, F. de L.; WEBER, V. A. de M.; MENEZES, G.; OLIVEIRA JUNIOR, A. da S.; ALVES, D. A.; OLIVEIRA, M. V. M. de; PISTORI, H.; ABREU, U. G. P. de A fim de propor um método de identificação não invasivo para o reconhecimento dos bovinos, este trabalho utilizou as redes neurais convolucionais (CNN), que é uma rede específica para processamento e... ... Repository: BDPA Publication year: 2019
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
Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. Author(s): SOUSA, I. C. de; NASCIMENTO, M.; SANT’ANNA, I. de C.; CAIXETA, E. T.; AZEVEDO, C. F.; CRUZ, C. D.; SILVA, F. L. da; ALKIMIM, E. R.; NASCIMENTO, A. C. C.; SERÃO, N. V. L. 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

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