Choi Woo-Joo Jang Se-Hun Moon Taewon Seo Kyeong-Su Choi Da-Seul Oh Myung-Min 2024-11-27T10:30:16Z 2024-11-27T10:30:16Z 2024-11-23 2024-11 https://pubs.kist.re.kr/handle/201004/151142 Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops. English MDPI AG Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer Article 10.3390/plants13213110 1 Plants, v.13, no.21 Plants 13 21 Y scie scopus 001351809900001 Plant Sciences Plant Sciences artificial intelligence computer vision crop growth indoor farming lettuce