Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology
Crop monitoring can promptly reflect the growth status of crops. However, conventional methods of growth monitoring, although simple and direct, have limitations such as destructive sampling, reliance on human experience, and slow detection speed. This study estimated the fresh weight of lettuce (&l...
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/29 |
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| author | Jun Ju Minggui Zhang Yingjun Zhang Qi Chen Yiting Gao Yangyue Yu Zhiqiang Wu Youzhi Hu Xiaojuan Liu Jiali Song Houcheng Liu |
| author_facet | Jun Ju Minggui Zhang Yingjun Zhang Qi Chen Yiting Gao Yangyue Yu Zhiqiang Wu Youzhi Hu Xiaojuan Liu Jiali Song Houcheng Liu |
| author_sort | Jun Ju |
| collection | DOAJ |
| description | Crop monitoring can promptly reflect the growth status of crops. However, conventional methods of growth monitoring, although simple and direct, have limitations such as destructive sampling, reliance on human experience, and slow detection speed. This study estimated the fresh weight of lettuce (<i>Lactuca sativa</i> L.) in a plant factory with artificial light based on three-dimensional (3D) reconstruction technology. Data from different growth stages of lettuce were collected as the training dataset, while data from different plant forms of lettuce were used as the validation dataset. The partial least squares regression (PLSR) method was utilized for modeling, and K-fold cross-validation was performed to evaluate the model. The testing dataset of this model achieved a coefficient of determination (R<sup>2</sup>) of 0.9693, with root mean square error (RMSE) and mean absolute error (MAE) values of 3.3599 and 2.5232, respectively. Based on the performance of the validation set, an adaptation was made to develop a fresh weight estimation model for lettuce under far-red light conditions. To simplify the estimation model, reduce estimation costs, enhance estimation efficiency, and improve the lettuce growth monitoring method in plant factories, the plant height and canopy width data of lettuce were extracted to estimate the fresh weight of lettuce in addition. The testing dataset of the new model achieved an R<sup>2</sup> value of 0.8970, with RMSE and MAE values of 3.1206 and 2.4576. |
| format | Article |
| id | doaj-art-56b29f661a974908bcb7ec381f8f7a64 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-56b29f661a974908bcb7ec381f8f7a642025-01-24T13:16:25ZengMDPI AGAgronomy2073-43952024-12-011512910.3390/agronomy15010029Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction TechnologyJun Ju0Minggui Zhang1Yingjun Zhang2Qi Chen3Yiting Gao4Yangyue Yu5Zhiqiang Wu6Youzhi Hu7Xiaojuan Liu8Jiali Song9Houcheng Liu10College of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCollege of Horticulture, South China Agricultural University, Guangzhou 510642, ChinaCrop monitoring can promptly reflect the growth status of crops. However, conventional methods of growth monitoring, although simple and direct, have limitations such as destructive sampling, reliance on human experience, and slow detection speed. This study estimated the fresh weight of lettuce (<i>Lactuca sativa</i> L.) in a plant factory with artificial light based on three-dimensional (3D) reconstruction technology. Data from different growth stages of lettuce were collected as the training dataset, while data from different plant forms of lettuce were used as the validation dataset. The partial least squares regression (PLSR) method was utilized for modeling, and K-fold cross-validation was performed to evaluate the model. The testing dataset of this model achieved a coefficient of determination (R<sup>2</sup>) of 0.9693, with root mean square error (RMSE) and mean absolute error (MAE) values of 3.3599 and 2.5232, respectively. Based on the performance of the validation set, an adaptation was made to develop a fresh weight estimation model for lettuce under far-red light conditions. To simplify the estimation model, reduce estimation costs, enhance estimation efficiency, and improve the lettuce growth monitoring method in plant factories, the plant height and canopy width data of lettuce were extracted to estimate the fresh weight of lettuce in addition. The testing dataset of the new model achieved an R<sup>2</sup> value of 0.8970, with RMSE and MAE values of 3.1206 and 2.4576.https://www.mdpi.com/2073-4395/15/1/29<i>Lactuca sativa</i>plant factory with artificial light3D reconstruction technologyfresh weight estimation |
| spellingShingle | Jun Ju Minggui Zhang Yingjun Zhang Qi Chen Yiting Gao Yangyue Yu Zhiqiang Wu Youzhi Hu Xiaojuan Liu Jiali Song Houcheng Liu Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology Agronomy <i>Lactuca sativa</i> plant factory with artificial light 3D reconstruction technology fresh weight estimation |
| title | Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology |
| title_full | Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology |
| title_fullStr | Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology |
| title_full_unstemmed | Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology |
| title_short | Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology |
| title_sort | development of lettuce growth monitoring model based on three dimensional reconstruction technology |
| topic | <i>Lactuca sativa</i> plant factory with artificial light 3D reconstruction technology fresh weight estimation |
| url | https://www.mdpi.com/2073-4395/15/1/29 |
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