マルチモーダルな産業用温室データからの自己教師あり学習による植物成長軌跡予測
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data
移動ロボットで大規模水耕栽培の環境・表現型データを収集し、自己教師あり学習で植物の成長軌跡を予測する手法を提案した。
著者: Adam J Riesselman, Evan M Cofer, Therese LaRue, Wim Meeussen
分類: q-bio.QM, cs.LG, cs.RO
原文アブストラクト
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.