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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
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
Identificação de marcadores SNPs para teor de amilose em arroz pela estratégia de Machine Learning. Author(s): SARTORI, D. E. L.; RODRIGUES, A. H. A.; CERRI, R.; BASSINELLO, P. Z.; NARCISO, M. G.; BRONDANI, C.; VIANELLO, R. P. O objetivo deste estudo foi identificar, por meio da estratégia Machine Learning, marcadores SNPs relacionados com TA para uso na seleção assistida por marcadores no programa de melhoramento genético... ... Repository: BDPA Publication year: 2018
Big earth observation data and machine learning for mapping crop-livestock integrated system in Brazil. Author(s): KUCHLER, P. C.; SIMÕES, M.; BEGUE, A.; FERRAZ, R. P. D. The adoption of crop-livestock (iCL) integrated systems has been pointed out as an important strategy for increasing production based on sustainable intensification of land use in Brazil. Mapping and... ... Repository: BDPA Publication year: 2021
Decision Support in Attribute Selection with Machine Learning Approach. Author(s): ARBEX, W. A.; SILVA, F. F. e; SILVA, M. V. G. B.; BORGES, C. C. H.; OLIVEIRA, F. C. de; VARONA, L.; VERNEQUE, R. da S. Repository: BDPA Publication year: 2014
Temporal field phenomics of transgenic maize events subjected to drought stress: cross?validation scenarios and machine learning models. Author(s): PEREIRA, H. D.; NONATO, J. V. A.; MOLTOCARO, R. C. R.; GERHARDT, I. R.; DANTE, R. A.; ARRUDA, P.; YASSITEPE, J. E. de C. T. Global climate change has driven breeding programs to develop abiotic stress?resilient plant varieties. Traditionally, assessing drought resilience involves labor?intensive and time?consuming processe... ... Repository: BDPA Publication year: 2025
Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2. Author(s): BOLFE, E. L.; PARREIRAS, T. C.; SILVA, L. A. P. da; SANO, E. E.; BETTIOL, G. M.; VICTORIA, D. de C.; DEL'ARCO SANCHES, I.; VICENTE, L. E. Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pre... ... Repository: BDPA Publication year: 2023
Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. Author(s): SOUSA, I. C. de; NASCIMENTO, M.; SILVA, G. N.; NASCIMENTO, A. C. C.; CRUZ, C. D.; SILVA, F. F. e; ALMEIDA, D. P. de; PESTANA, K. N.; AZEVEDO, C. F.; ZAMBOLIM, L.; CAIXETA, E. T. Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the p... ... Repository: BDPA Publication year: 2021
Drought tolerance classification of grapevine rootstock by machine learning for the São Francisco Valley. Author(s): VERSLYPEA, N. I.; NASCIMENTO, A. C. A. do; MUSSER, R. dos S.; CALDASM R, M. de S.; MARTINS, L. S. S.; LEAO, P. C. de S. Machine Learning (ML) algorithms are increasingly being used in several areas of agricultural studies, such as plant breeding. ML can assist in the recognition of relevant patterns or groups, or even... ... Repository: BDPA Publication year: 2023

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