Lettuce growth predicted by individual leaf climate model
From greenhouse climate to individual leaves: an organ-resolved model of lettuce growth
Computer Vision and Pattern RecognitionGraphics
Summary
Greenhouses need to control climate to grow plants well and save energy, but they must know how plants will react to changes. This paper describes a detailed computer model that simulates how each lettuce leaf grows and uses sunlight inside a greenhouse. By tracking leaf growth and light exposure, the model accurately predicts overall lettuce growth and problems like tipburn. The model helps understand how conditions affect plants differently in crowded greenhouses and can improve climate decisions.
What this means in practice
- •For greenhouse operators: Adjust greenhouse climate settings using detailed plant growth predictions based on individual leaf behavior to optimize yield and reduce energy costs.
- •For agronomic modelers: Incorporate detailed leaf-level light and growth data from this model into larger crop simulations for more precise predictions of biomass and stress.
Authors
Md Hasibur Rahman, Faraz Ahmed, Hafiz Muhammad Bilal, Daniel Wells, Dylan Tobin, Tanzeel U. Rehman
Abstract
Greenhouse climate management aims to improve crop production while limiting energy use. This requires knowing how a crop will respond before conditions are changed. A crop digital twin can support this decision only if it represents how plant physiology and structure develop together. A unified framework was developed to simulate lettuce growth from the physiology of individual leaves. Each leaf received the conditions at its position in the canopy and contributed carbon through photosynthesis. Part of this carbon was used for maintenance and the remainder supported growth, distributed among leaves by their age, size and local environment. The predicted leaf mass, area and age generated an evolving three-dimensional plant in NVIDIA Isaac Sim. Ray tracing calculated the radiation intercepted by each leaf and returned it to photosynthesis, so structure and growth influenced each other over time. Against greenhouse measurements, the relative root mean square error was 9.5% for total dry weight and 9.2%, 12.7% and 13.1% for leaf number, canopy diameter and largest-leaf area, respectively. A 30% decrease in incident radiation reduced final dry weight by 10.4%, while the same increase raised it by 6.9%, and adding 200 ppm carbon dioxide raised it by 46.1%. Within a simulated 40-plant block, interior plants accumulated 8.6% less dry weight than border plants with identical initial states, and the leaf-specific tipburn index rose in the enclosed leaves over the period in which tipburn appeared on the greenhouse plants. Resolving individual leaves therefore explains how local exposure changes plant growth within the greenhouse. The framework provides the forward plant model needed for a bidirectional digital twin, where observations of the physical plant can update predictions and support greenhouse climate decisions.