Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15640%2F25%3A73630770" target="_blank" >RIV/61989592:15640/25:73630770 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/25:10260368
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S016816992500496X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S016816992500496X?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.compag.2025.110390" target="_blank" >10.1016/j.compag.2025.110390</a>
Alternative languages
Result language
angličtina
Original language name
Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence
Original language description
Phenotypes, which define an organism's behaviour and physical attributes, result from the complex interplay of genetics, development, and environment. Predicting future plant traits is mainly challenging due to these dynamic interactions. This work presents AMULET, a modular approach that combines imaging-based highthroughput phenotyping and machine learning to predict morphological and physiological plant traits hours to days before they are visible. Trained with over 30,000 Arabidopsis thaliana plants, AMULET streamlines the phenotyping process by integrating plant detection, prediction, segmentation, and data analysis, enhancing workflow efficiency and reducing time. AMULET achieved impressive performance metrics, including a dice loss of 0.0104 and an IoU score of 0.9948 for the test set, indicating high accuracy in the segmentation task or an R2 score of 0.9289 for descriptor estimation. Moreover, Simpler yet Better Video Prediction (SimVP) appeared as the most effective model in predicting plant growth and health status. Using phenotyping images from studies focused on the Arabidopsis thaliana-Pseudomonas syringae pathosystem, AMULET analysed the latent phenom by identifying traits restrictive to human perception and essential to understanding plant response to concrete growth conditions. Techniques like TorchGrad and Gradient-weighted Class Activation Mapping helped to reveal these new hidden traits. AMULET also demonstrated its adaptability by accurately detecting and predicting phenotypes of in vitro potato plants after minimal fine-tuning with just 100 plants. This versatile approach streamlines phenotyping and holds significant promise for improving breeding programs and agricultural management by enabling pre-emptive interventions optimising plant health and productivity.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN
0168-1699
e-ISSN
1872-7107
Volume of the periodical
235
Issue of the periodical within the volume
August
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
nestránkováno
UT code for WoS article
001470890700001
EID of the result in the Scopus database
2-s2.0-105002402756