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Deep learning for soybean monitoring and management. Author(s): BARBEDO, J. G. A. This review characterizes the current state of the art of deep learning applied to soybean crops, detailing the main advancements achieved so far and, more importantly, providing an in-depth analysis... ... Repository: BDPA Publication year: 2023
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
A review of deep learning algorithms for computer vision systems in livestock. Author(s): PEREIRA, L. G. R. In livestock operations, systematically monitoring animal body weight, bio-metric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to... ... Repository: BDPA Publication year: 2021
Social learning as an underlying mechanism for sustainability in neglected communities: the Brazilian case of the Bucket Revolution project. Author(s): TURETTA, A. P. D. In neglected communities, waste and organic residues are not only a vector of several problems, like diseases and water pollution, but also a contributor to increasing forms of vulnerability and margi... ... Repository: BDPA Publication year: 2022
An analytical model of the relationship knowledge, learning, innovation and sustainability. Author(s): CABRAL, J. E. de O.; COSTA, M. da P. B. Repository: BDPA Publication year: 2010
Forecasting insect abundance using time series embedding and machine learning. Author(s): PALMA, G. R.; MELLO, R. F.; GODOY, W. A.; ENGEL, E.; LAU, D.; MARKHAM, C.; MORAL, R. A. Implementing insect monitoring systems provides an excellent opportunity to create accurate interventions for insect control. However, selecting the appropriate time for an intervention is still an op... ... Repository: BDPA Publication year: 2024
Detecting and classifying pests in crops using proximal images and machine learning: a review. Author(s): BARBEDO, J. G. A. Abstract: Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable re... ... Repository: BDPA Publication year: 2020
Analysis of pest incidence on apple trees validated by unsupervised machine learning algorithms. Author(s): SPERANZA, E. A.; GREGO, C. R.; GEBLER, L. ABSTRACT. Integrated pest control is a practice commonly used in apple orchards in southern Brazil. This type of management is an important tool to help improve quality and increase yields. This study... ... Repository: BDPA Publication year: 2022
Laser-induced breakdown spectroscopy associated with the design of experiments and machine learning for discrimination of Brachiaria brizantha seed vigor. Author(s): CIOCCIA, G.; MORAIS, C. P. de; BABOS, D. V.; MILORI, D. M. B. P.; ALVES, C. Z.; CENA, C.; NICOLODELLI, G.; MARANGONI, B. S. Laser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the Brachiaria seed physiological quality by discriminating the high and low vigor see... ... Repository: BDPA Publication year: 2022
Utilização de machine learning para classificação de plantas saudáveis e plantas submetidas ao estresse. Author(s): MILORI, D. M. B. P. A integração de tecnologias na agricultura tem se tornado cada vez mais comum, trazendo consigo uma série de benefícios, desde o aumento da produtividade até contribuições para a sustentabilidade. Uma... ... Repository: BDPA Publication year: 2023

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