Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Loladze, Alexander; Rodrigues, Francelino A.; Petroli, Cesar D.; Muñoz-Zavala, Carlos; Naranjo, Sergio; San Vicente, Felix; Gerard, Bruno; Montesinos-Lopez, Osval A.; Crossa, Jose; Martini, Johannes W.R.

doi:10.1016/j.fcr.2024.109281

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2024

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Cited by 2 publications

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Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations

Loladze,

Rodrigues,

Petroli

et al. 2024

Data in Brief

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Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations

Loladze,

Rodrigues,

Petroli

et al. 2024

Data in Brief

View full text Add to dashboard Cite

Automatic identification method of surface lithology based on remote sensing satellite data and machine learning

Wang

2024

Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024)

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Multi-source remote sensing data fusion could compensate for the lack of spatial or spectral resolution of single-source remote sensing images. In this paper, we took the rocky area as the research object and carried out a machine learning lithology classification study based on combining spatial features and spectral features, and proposed an automatic identification method of surface lithology based on limit learning and watershed segmentation. By comparing and analyzing typical machine learning algorithms, the combination of spectral features and spatial features was incorporated into the algorithm to form a set of high-precision lithology automatic identification methods. The experimental results showed that the method in this paper performed better than the traditional SVM classification method based on spectral features in terms of classification accuracy and Kappa coefficient, especially after the extreme learning fusion watershed segmentation algorithm.

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Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Cited by 2 publications

References 40 publications

Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations

Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations

Automatic identification method of surface lithology based on remote sensing satellite data and machine learning

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