Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer
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| Publicado en: | Plants vol. 13, no. 21 (2024), p. 3110 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3126033617 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2223-7747 | ||
| 024 | 7 | |a 10.3390/plants13213110 |2 doi | |
| 035 | |a 3126033617 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231551 |2 nlm | ||
| 100 | 1 | |a Woo-Joo, Choi |u Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea; <email>ujuchoe79@chungbuk.ac.kr</email> (W.-J.C.); <email>zsh8976@naver.com</email> (S.-H.J.); <email>nari4491@naver.com</email> (K.-S.S.); <email>daseul7312@gmail.com</email> (D.-S.C.) | |
| 245 | 1 | |a Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 610 | 4 | |a Raspberry Pi Ltd | |
| 651 | 4 | |a South Korea | |
| 651 | 4 | |a United States--US | |
| 653 | |a Resource efficiency | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Leaf area | ||
| 653 | |a Humidity | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Nondestructive testing | ||
| 653 | |a Investigations | ||
| 653 | |a Simulation models | ||
| 653 | |a Color imagery | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Developmental stages | ||
| 653 | |a Productivity | ||
| 653 | |a Crops | ||
| 653 | |a Image processing | ||
| 653 | |a Feedback | ||
| 653 | |a Control systems | ||
| 653 | |a Computer vision | ||
| 653 | |a Monitoring | ||
| 653 | |a Lettuce | ||
| 653 | |a Growth factors | ||
| 653 | |a Crop growth | ||
| 653 | |a Farming | ||
| 653 | |a Image enhancement | ||
| 653 | |a Predictions | ||
| 653 | |a Neural networks | ||
| 653 | |a Data collection | ||
| 653 | |a Image quality | ||
| 653 | |a Real time | ||
| 653 | |a Feedback control | ||
| 700 | 1 | |a Se-Hun Jang |u Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea; <email>ujuchoe79@chungbuk.ac.kr</email> (W.-J.C.); <email>zsh8976@naver.com</email> (S.-H.J.); <email>nari4491@naver.com</email> (K.-S.S.); <email>daseul7312@gmail.com</email> (D.-S.C.) | |
| 700 | 1 | |a Moon, Taewon |u Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Republic of Korea; <email>tmoon.hort@kist.re.kr</email> | |
| 700 | 1 | |a Kyeong-Su Seo |u Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea; <email>ujuchoe79@chungbuk.ac.kr</email> (W.-J.C.); <email>zsh8976@naver.com</email> (S.-H.J.); <email>nari4491@naver.com</email> (K.-S.S.); <email>daseul7312@gmail.com</email> (D.-S.C.) | |
| 700 | 1 | |a Da-Seul, Choi |u Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea; <email>ujuchoe79@chungbuk.ac.kr</email> (W.-J.C.); <email>zsh8976@naver.com</email> (S.-H.J.); <email>nari4491@naver.com</email> (K.-S.S.); <email>daseul7312@gmail.com</email> (D.-S.C.) | |
| 700 | 1 | |a Myung-Min, Oh |u Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea; <email>ujuchoe79@chungbuk.ac.kr</email> (W.-J.C.); <email>zsh8976@naver.com</email> (S.-H.J.); <email>nari4491@naver.com</email> (K.-S.S.); <email>daseul7312@gmail.com</email> (D.-S.C.) | |
| 773 | 0 | |t Plants |g vol. 13, no. 21 (2024), p. 3110 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3126033617/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3126033617/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3126033617/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |