Automatic Ecological Control and Mathematical Growth Prediction Models for Lettuce Seedling Nursery System

Abstract

The research introduces an automatic nursery machine designed to enhance lettuce (green oak salad) seedling cultivation by regulating environmental conditions. The goal is to produce higher-quality lettuce in unfavourable settings. The study outlines two key components of this automatic ecological system: the environmental device design for lettuce control and a mathematical growth prediction model to support the machine's operation. The first component employs an Arduino microcontroller equipped with sensors to manage and accelerate the growth of nursery lettuce. The second aspect concentrates on growth prediction modelling, which informs and regulates the lettuce seedling nursery system. The automatic ecological system is implemented and tested against the community enterprise (CE) method, demonstrating superior results. The lettuce seedlings cultivated with the automatic nursery machine exhibit thicker, stronger stems, larger leaves, and a higher germination rate of 9.18% compared to the CE method. For the mathematical growth prediction models, multiple regression models are developed to correlate lettuce height (H) and stem width (W) with temperature, relative humidity, and light intensity within the automatic nursery machine. The goodness-of-fit analyses indicate reasonable model fits with R², MSE, and RMSE values of (W = 0.521, 0.093, 0.305,
H = 0.604, 28.025, 5.294), respectively. Therefore, the automatic nursery machine offers an effective means to accelerate lettuce growth, potentially opening opportunities for large-scale industrial applications.