Estimation of Days after Transplanting using an Artificial Intelligence CNN (Convolutional Neural Network) Model in a Closed-type Plant Factory

It is important to measure the growth status in real time during the cultivation of crops. In a fully controlled plant factory, the number of days after transplanting (DAT) can be an indicator of growth status due to accurate environmental management and the uniform growth of cultivated plants. This study was conducted to estimate the number of DAT of lettuce through image data using an artificial intelligence model in a closed plant factory system. The seedlings used were the 'Thimble' variety of green lettuce from Nunhems, and image data were collected according to the number of DAT. The RGB ratio was set to 8:1:1, and the amount of light was adjusted to 265(±50) μmol·m-2·s-1 with a photoperiod of 12 hours. The cultivation environment maintained a temperature of 19–21°C, relative humidity of 55–65%, and CO2 in the range of 500–800 μmol·mol-1. Through three experiments conducted in identical environments, images were taken according to the date of growth from 12 specimens of ‘Thimble’ in total. In all three duplicated experiments, images were collected 4th, 8th, 12th, 15th, 18th, and 22nd DAT. Among collected image set, 240 images from ten objects were used for learning dataset and 48 images from two objects were used to test dataset the accuracy of the generated artificial intelligence model. The artificial intelligence model created through the convolutional neural network (CNN) generated with Python's TensorFlow had a test accuracy rate of 91.7%, and the artificial intelligence model created through a teachable machine showed accuracy of 86.7% for the test samples in this experiment. The number of DAT was predicted with the maximum probability. In this research, although the artificial intelligence model was created with a small amount of image data, it showed significantly high accuracy due to the characteristics of the plant factory where standardized image data were produced by precise environmental control and the uniform growth of the cultivated plants. Given the nature of the artificial neural network, which increase the accuracy of the model as more data are inputted, it is expected that the artificial intelligence model will become more precise if more image data can be collected through additional experiments in the future. Through these efforts, it will be possible to have a system capable of checking and offering feedback of the growth of cultivated plants in real time by introducing artificial intelligence prediction and discrimination to the plant factory cultivation site.