Publications

8 Papers

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Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum.

Wu K, Zhang Z, He X, Li G, Zheng D, Li Z

Published: 19 February 2025 in Scientific reports
Keywords:
Pubmed ID: 39972103
DOI: 10.1038/s41598-025-90892-6

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Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan.

Karongo J, Mwaniki JI, Ndiritu J, Mokaya V

Published: 6 February 2025 in Scientific reports
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Pubmed ID: 39915691
DOI: 10.1038/s41598-025-89030-z

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Methylomes as key features for predicting recombination in some plant species.

PeƱuela M, Finke J, Rocha C

Published: 8 March 2024 in Plant molecular biology
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Pubmed ID: 38457042
DOI: 10.1007/s11103-023-01396-8

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Optimization of biomethane production from sweet sorghum bagasse using artificial neural networks combined with particle swarm algorithm.

Machineni L, Anupoju GR

Published: 19 October 2023 in Environmental science and pollution research international
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Pubmed ID: 37855961
DOI: 10.1007/s11356-023-30451-6

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Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation model.

Genze N, Wirth M, Schreiner C, Ajekwe R, Grieb M, Grimm DG

Published: 23 August 2023 in Plant methods
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Pubmed ID: 37608384
DOI: 10.1186/s13007-023-01060-8

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Multi-Species Prediction of Physiological Traits with Hyperspectral Modeling.

Lin MY, Lynch V, Ma D, Maki H, Jin J, Tuinstra M

Published: 11 March 2022 in Plants (Basel, Switzerland)
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Pubmed ID: 35270145
DOI: 10.3390/plants11050676

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Hyperspectral plant sensing for differentiating glyphosate-resistant and glyphosate-susceptible johnsongrass through machine learning algorithms.

Huang Y, Zhao X, Pan Z, Reddy KN, Zhang J

Published: 8 March 2022 in Pest management science
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Pubmed ID: 35254728
DOI: 10.1002/ps.6864

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Application of X-ray computed tomography to analyze the structure of sorghum grain.

Crozier D, Riera-Lizarazu O, Rooney WL

Published: 13 January 2022 in Plant methods
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Pubmed ID: 35016682
DOI: 10.1186/s13007-022-00837-7

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