Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data
This work addresses the challenge of scaling plant phenotype data for agronomic research and operational efficiency in hydroponic systems, representing an incremental advance in combining robotics and machine learning.
The researchers tackled the problem of predicting plant growth trajectories in industrial greenhouses by developing a self-supervised learning model that forecasts future plant height and harvest mass, achieving results that provide actionable insights for crop optimization.
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.