Digital twin for lettuce growth in six climate zones (‘Climate 2050′)

Abstract

This study investigates lettuce growth under extreme environmental conditions by simulating the weather in six climate zones in a plant growth chamber, including Lleida, Adelaide, Paris, San Luis, Singapore, and Fairbanks. The experiment involved weekly exposure to a new city’s climate, simulating “non-terrestrial weather stress,” which is also motivated from the vantage point of space plant growth and its process-control limitations. These simulated conditions shed light on ‘Climate 2050’, when Earth will probably have harsher and more fluctuating conditions. For the period investigated, the real temperature changes could be reproduced well and in real-time in the growth chamber, the actual rain fall was mimicked, and the lighting period was adjusted to the real sunshine exposure in the respective city. The virtual move of the lettuce plant from between six climates with their own profile in temperature, lighting time, and water is assumed to create stress beyond the variability of a weather change within a single climate. Machine learning models, including linear regression, random forest regression, and boosted decision tree regression, were employed to predict weekly lettuce biomass and yield. This study successfully demonstrated the application of machine learning algorithms for predicting lettuce growth under the given range of six climate conditions. Among the tested models, random forest regression consistently delivered the most accurate and reliable biomass predictions, achieving an R² of nearly 99 % and MAPE of 6 % in all scenarios. By introducing tuned correction factors for conditions like drought stress, fertilisation, and mixed soil composition, the accuracy and flexibility of models are enhanced. This research highlights the value of integrating real-time data with machine learning through a digital twin framework, offering a promising direction for climate-resilient agriculture and space-based plant growth systems.

Plant phenotyping relevance

デジタルツインと機械学習を中核に、レタスのバイオマスおよび収量を予測する再利用可能な計算ワークフローを構築・評価しており、植物形質推定法が中心である。

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