UAV-based individual Chinese cabbage weight prediction using multi-temporal data. Aguilar-Ariza, A., Ishii, M., Miyazaki, T., Saito, A., Khaing, H. P., Phoo, H. W., Kondo, T., Fujiwara, T., Guo, W., & Kamiya, T. Scientific Reports, 13(1):20122, November, 2023. ![UAV-based individual Chinese cabbage weight prediction using multi-temporal data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R 2 ) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R 2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.
@article{aguilar-ariza_uav-based_2023,
title = {{UAV}-based individual {Chinese} cabbage weight prediction using multi-temporal data},
volume = {13},
issn = {2045-2322},
url = {https://www.nature.com/articles/s41598-023-47431-y},
doi = {10.1038/s41598-023-47431-y},
abstract = {Abstract
The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95\% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R
2
) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R
2
greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.},
language = {en},
number = {1},
urldate = {2023-11-30},
journal = {Scientific Reports},
author = {Aguilar-Ariza, Andrés and Ishii, Masanori and Miyazaki, Toshio and Saito, Aika and Khaing, Hlaing Phyoe and Phoo, Hnin Wint and Kondo, Tomohiro and Fujiwara, Toru and Guo, Wei and Kamiya, Takehiro},
month = nov,
year = {2023},
pages = {20122},
}
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In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R 2 ) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R 2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.","language":"en","number":"1","urldate":"2023-11-30","journal":"Scientific Reports","author":[{"propositions":[],"lastnames":["Aguilar-Ariza"],"firstnames":["Andrés"],"suffixes":[]},{"propositions":[],"lastnames":["Ishii"],"firstnames":["Masanori"],"suffixes":[]},{"propositions":[],"lastnames":["Miyazaki"],"firstnames":["Toshio"],"suffixes":[]},{"propositions":[],"lastnames":["Saito"],"firstnames":["Aika"],"suffixes":[]},{"propositions":[],"lastnames":["Khaing"],"firstnames":["Hlaing","Phyoe"],"suffixes":[]},{"propositions":[],"lastnames":["Phoo"],"firstnames":["Hnin","Wint"],"suffixes":[]},{"propositions":[],"lastnames":["Kondo"],"firstnames":["Tomohiro"],"suffixes":[]},{"propositions":[],"lastnames":["Fujiwara"],"firstnames":["Toru"],"suffixes":[]},{"propositions":[],"lastnames":["Guo"],"firstnames":["Wei"],"suffixes":[]},{"propositions":[],"lastnames":["Kamiya"],"firstnames":["Takehiro"],"suffixes":[]}],"month":"November","year":"2023","pages":"20122","bibtex":"@article{aguilar-ariza_uav-based_2023,\n\ttitle = {{UAV}-based individual {Chinese} cabbage weight prediction using multi-temporal data},\n\tvolume = {13},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-023-47431-y},\n\tdoi = {10.1038/s41598-023-47431-y},\n\tabstract = {Abstract\n \n The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95\\% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R\n 2\n ) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R\n 2\n greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. 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