Data-Driven Optimization of Substrate Composition for Lettuce in Soilless Cultivation

Soilless cultivation has emerged as a sustainable solution for modern agriculture, yet substrate formulation is still often guided by empirical approaches, limiting efficiency and reproducibility. To address this gap, we established a data-driven framework for optimizing substrate composition in garden lettuce (Lactuca sativa L.) cultivation. Using a randomized design, 200 substrate formulations were prepared from peat, vermiculite, and perlite, and their effects on plant growth were evaluated under controlled environmental conditions. Peat content reduced substrate porosity and water-holding capacity, whereas vermiculite increased both properties (linear regression, p < 0.05). Substrate formulations profoundly affected plant biomass, and the peat content was identified as a key predictor. Two rounds of substrate optimization resulted in a significant increase in shoot and root biomass and chlorophyll content, with increases of 57.5% (p = 9.2 × 10−8), 89.8% (p = 8.24 × 10−10), and 43.3% (p < 2 × 10−16), respectively, compared with the initial trial. Additionally, hyperspectral imaging (HSI) and RGB imaging were employed for growth monitoring. Random forest machine-learning method identified several red-edge indices (NDVI705, mNDVI705, mSR705) as highly responsive predictors of substrate formulations, highlighting the potential of imaging traits as proxies for substrate optimization. This study provides a reproducible pathway for improving soilless substrate formulations, contributing to data-informed substrate design and advancing the practice of precision agriculture.