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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): OLIVEIRA, D. A. B.; PEREIRA, L. G. R.; BRESOLIN, T.; FERREIRA, R. E. P.; DREA, J. R. 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): BONATTI, M.; ERISMANN, C.; ASKHABALIEVA, A.; BORBA, J.; POPE, K.; REYNALDO, R.; EUFEMIA, L.; TURETTA, A. P. D.; SIEBER, S. 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
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
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

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