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Forecasting insect abundance using time series embedding and machine learning. Author(s): LAU, D. 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): MILORI, D. M. B. P. 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
Identificação de marcadores SNPs para teor de amilose em arroz pela estratégia de Machine Learning. Author(s): 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
Machine learning prediction of multiple anthelmintic resistance and gastrointestinal nematode control in sheep flocks. Author(s): NICIURA, S. C. M. The high prevalence of Haemonchus contortus and its anthelmintic resistance have affected sheep production worldwide. Machine learning approaches are able to investigate the complex relationships amon... ... Repository: BDPA Publication year: 2024
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): MOLTOCARO, R. C. R.; GERHARDT, I. R.; DANTE, R. A.; 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

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