用自监督学习预测植物生长轨迹，无需大量标注数据。
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data
- 通过机器人采集多模态数据，构建生长轨迹预测模型。
- 可准确预报作物未来株高与收获质量，提升农业效率。
- 适合智能农业、自动化育种研究者参考。
量化生物体水平的表型，如生长动态和生物量积累，是理解农艺性状与优化作物生产的基础。然而，大规模高质量的植物生长数据难以获取。本文利用移动机器人平台，对大规模水培叶生菜系统进行高分辨率环境感知与表型测量。提出一种自监督建模方法，将观测生长数据映射至完整的植物生长轨迹。通过该方法实现了对作物未来株高和收获质量的预测。该方法融合了机器人自动化与机器学习技术，显著推动了农业研究与运营效率的提升。
原文摘要 · Abstract (English)
Quantifying organism-level phenotypes, such as growth dynamics and biomass accumulation, is fundamental to understanding agronomic traits and optimizing crop production. However, quality growing data of plants at scale is difficult to generate. Here we use a mobile robotic platform to capture high-resolution environmental sensing and phenotyping measurements of a large-scale hydroponic leafy greens system. We describe a self-supervised modeling approach to build a map from observed growing data to the entire plant growth trajectory. We demonstrate our approach by forecasting future plant height and harvest mass of crops in this system. This approach represents a significant advance in combining robotic automation and machine learning, as well as providing actionable insights for agronomic research and operational efficiency.
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