Machine Learning-Driven Construction of High-Yielding Cucumber Plant Architectures in Greenhouse Environments.

Journal: Plant biotechnology journal
Published Date: Jan 12, 2026

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 (R2 = 0.6155, RMSE = 0.2601). Analysis of 157 464 phenotypic combinations revealed antagonistic interactions between robust aboveground structures and fine root systems, and synergistic interactions between slender aboveground parts and broad root systems. Yields were up to 20% higher in phenotypes that combined a compact, robust aboveground structure with a narrow yet larger-diameter and shallower root system, reflecting additive effects rather than synergistic ones. Additionally, this study proposes a reference range for high-yielding phenotypes. Overall, this research provides a theoretical foundation for optimising cucumber plant structures under greenhouse environments by predicting yields and investigating phenotypic interactions through modelling.

Authors

  • Cuifang Zhu
    State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
  • Hongjun Yu
    State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
  • Caili Zhao
    College of Horticulture, Xinjiang Agricultural University, Urumqi, China.
  • Hongyang Wu
    Department of Radiology, Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine, No. 2, Zifang Lane, Hero South Road, Luzhou District, Changzhi, 046000, People's Republic of China.
  • Xiaoyang Wan
    State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Tao Lu
    Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Weijie Jiang
    State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
  • Qiang Li
    Department of Dermatology, Air Force Medical Center, PLA, Beijing, People's Republic of China.

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