Integrating non-invasive VIS-NIR and bioimpedance spectroscopies for stress classification of sweet basil (Ocimum basilicum L.) with machine learning

Son, Daesik; Park, Junyoung; Lee, Siun; Kim, Jae Joon; Chung, Soo

doi:10.1016/j.bios.2024.116579

Biosensors and Bioelectronics

2024

DOI: 10.1016/j.bios.2024.116579

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Integrating non-invasive VIS-NIR and bioimpedance spectroscopies for stress classification of sweet basil (Ocimum basilicum L.) with machine learning

Daesik Son,

Junyoung Park,

Siun Lee

et al.

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Decrypting the complex phenotyping traits of plants by machine learning

Zdrazil,

Kong,

Klimeš

et al. 2024

Preprint

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Phenotypes, defining an organism's behaviour and physical attributes, arise from the complex, dynamic interplay of genetics, development, and environment, whose interactions make it enormously challenging to forecast future phenotypic traits of a plant at a given moment. This work reports AMULET, a modular approach that uses imaging-based high-throughput phenotyping and machine learning to predict morphological and physiological plant traits hours to days before they are visible. AMULET streamlines the phenotyping process by integrating plant detection, prediction, segmentation, and data analysis, enhancing workflow efficiency and reducing time. The machine learning models used data from over 30,000 plants, using the Arabidopsis thaliana-Pseudomonas syringae pathosystem. AMULET also demonstrated its adaptability by accurately detecting and predicting phenotypes of in vitro potato plants after minimal fine-tuning with a small dataset. The general approach implemented through AMULET streamlines phenotyping and will improve breeding programs and agricultural management by enabling pre-emptive interventions optimising plant health and productivity.

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Decrypting the complex phenotyping traits of plants by machine learning

Zdrazil,

Kong,

Klimeš

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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Integrating non-invasive VIS-NIR and bioimpedance spectroscopies for stress classification of sweet basil (Ocimum basilicum L.) with machine learning

Cited by 1 publication

References 31 publications

Decrypting the complex phenotyping traits of plants by machine learning

Decrypting the complex phenotyping traits of plants by machine learning

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