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Volume 13
Issue 21
10.3390/plants13213110
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Article

Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer

by
Woo-Joo Choi
1,
Se-Hun Jang
1,
Taewon Moon
2,
Kyeong-Su Seo
1,
Da-Seul Choi
1 and
Myung-Min Oh
1,*
1
Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea
2
Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Republic of Korea
*
Author to whom correspondence should be addressed.
Plants 2024, 13(21), 3110; https://doi.org/10.3390/plants13213110
Submission received: 22 September 2024 / Revised: 30 October 2024 / Accepted: 31 October 2024 / Published: 4 November 2024
(This article belongs to the Special Issue Predicting and Understanding Plant Properties with Data-Driven Approaches)
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Abstract

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.
Keywords: artificial intelligence; computer vision; crop growth; indoor farming; lettuce artificial intelligence; computer vision; crop growth; indoor farming; lettuce

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MDPI and ACS Style

Choi, W.-J.; Jang, S.-H.; Moon, T.; Seo, K.-S.; Choi, D.-S.; Oh, M.-M. Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer. Plants 2024, 13, 3110. https://doi.org/10.3390/plants13213110

AMA Style

Choi W-J, Jang S-H, Moon T, Seo K-S, Choi D-S, Oh M-M. Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer. Plants. 2024; 13(21):3110. https://doi.org/10.3390/plants13213110

Chicago/Turabian Style

Choi, Woo-Joo, Se-Hun Jang, Taewon Moon, Kyeong-Su Seo, Da-Seul Choi, and Myung-Min Oh. 2024. "Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer" Plants 13, no. 21: 3110. https://doi.org/10.3390/plants13213110

APA Style

Choi, W.-J., Jang, S.-H., Moon, T., Seo, K.-S., Choi, D.-S., & Oh, M.-M. (2024). Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer. Plants, 13(21), 3110. https://doi.org/10.3390/plants13213110

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