ArticlePlant biotechnology journal2026
Machine Learning-Driven Construction of High-Yielding Cucumber Plant Architectures in Greenhouse Environments.
Article in Plant biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Negative Regulators of Rice Agronomic Traits: Functional Insights and Applications in Genome Editing-Based Breeding.Plant biotechnology journal · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
In the context of declining arable land, the development of plant architectures that maximise the use of finite resources is crucial for addressing food security. This study collected yield data, along with aboveground and root traits, from 263 cucumber varieties. Machine learning models and scenario simulations were utilised with the goal of identifying a high-yielding cucumber architecture suitable for greenhouse cultivation. Our findings indicate that cucumber yields can be predicted using aboveground and root phenotypes, such as the position of the first female flower node, leaf width, stem diameter, and root angle, with the combination of GBDT and SVM algorithms yielding the most accurate results (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.