Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors
Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making. However, traditional methods are constrained by reliance on empirical knowledge, time-consuming processes, resource intensiveness, and spatial-temporal variability in prediction ac...
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| Format: | Article |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000236 |
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| author | Qing Wang Ke Shao Zhibo Cai Yingpu Che Haochong Chen Shunfu Xiao Ruili Wang Yaling Liu Baoguo Li Yuntao Ma |
| author_facet | Qing Wang Ke Shao Zhibo Cai Yingpu Che Haochong Chen Shunfu Xiao Ruili Wang Yaling Liu Baoguo Li Yuntao Ma |
| author_sort | Qing Wang |
| collection | DOAJ |
| description | Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making. However, traditional methods are constrained by reliance on empirical knowledge, time-consuming processes, resource intensiveness, and spatial-temporal variability in prediction accuracy. This study presented a plot-level approach that leverages UAV technology and recurrent neural networks to provide field yield predictions within the same growing season, addressing a significant gap in previous research that often focuses on regional scale predictions relied on multi-year history datasets. End-of-season yield and quality parameters were forecasted using UAV-derived time series data and meteorological factors collected at three critical growth stages, providing a timely and practical tool for farm management. Two years of data covering 185 sugar beet varieties were used to train a developed stacked Long Short-Term Memory (LSTM) model, which was compared with traditional machine learning approaches. Incorporating fresh weight estimates of aboveground and root biomass as predictive factors significantly enhanced prediction accuracy. Optimal performance in prediction was observed when utilizing data from all three growth periods, with R2 values of 0.761 (rRMSE = 7.1 %) for sugar content, 0.531 (rRMSE = 22.5 %) for root yield, and 0.478 (rRMSE = 23.4 %) for sugar yield. Furthermore, combining data from the first two growth periods shows promising results for making the predictions earlier. Key predictive features identified through the Permutation Importance (PIMP) method provided insights into the main factors influencing yield. These findings underscore the potential of using UAV time-series data and recurrent neural networks for accurate pre-harvest yield prediction at the field scale, supporting timely and precise agricultural decisions. |
| format | Article |
| id | doaj-art-924127fce8f441aa824b481deddb0526 |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-924127fce8f441aa824b481deddb05262025-08-20T03:44:28ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-06-0115225226510.1016/j.aiia.2025.02.004Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factorsQing Wang0Ke Shao1Zhibo Cai2Yingpu Che3Haochong Chen4Shunfu Xiao5Ruili Wang6Yaling Liu7Baoguo Li8Yuntao Ma9College of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaInner Mongolia Academy of Science and Technology, Hohhot 010010, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaInner Mongolia Academy of Science and Technology, Hohhot 010010, ChinaInner Mongolia Pratacultural Technology Innovation Center Co. Ltd, Inner Mongolia, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China; Corresponding author.Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making. However, traditional methods are constrained by reliance on empirical knowledge, time-consuming processes, resource intensiveness, and spatial-temporal variability in prediction accuracy. This study presented a plot-level approach that leverages UAV technology and recurrent neural networks to provide field yield predictions within the same growing season, addressing a significant gap in previous research that often focuses on regional scale predictions relied on multi-year history datasets. End-of-season yield and quality parameters were forecasted using UAV-derived time series data and meteorological factors collected at three critical growth stages, providing a timely and practical tool for farm management. Two years of data covering 185 sugar beet varieties were used to train a developed stacked Long Short-Term Memory (LSTM) model, which was compared with traditional machine learning approaches. Incorporating fresh weight estimates of aboveground and root biomass as predictive factors significantly enhanced prediction accuracy. Optimal performance in prediction was observed when utilizing data from all three growth periods, with R2 values of 0.761 (rRMSE = 7.1 %) for sugar content, 0.531 (rRMSE = 22.5 %) for root yield, and 0.478 (rRMSE = 23.4 %) for sugar yield. Furthermore, combining data from the first two growth periods shows promising results for making the predictions earlier. Key predictive features identified through the Permutation Importance (PIMP) method provided insights into the main factors influencing yield. These findings underscore the potential of using UAV time-series data and recurrent neural networks for accurate pre-harvest yield prediction at the field scale, supporting timely and precise agricultural decisions.http://www.sciencedirect.com/science/article/pii/S2589721725000236Sugar beet yieldTime-series dataRecurrent neural networkUAVMeteorological factors |
| spellingShingle | Qing Wang Ke Shao Zhibo Cai Yingpu Che Haochong Chen Shunfu Xiao Ruili Wang Yaling Liu Baoguo Li Yuntao Ma Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors Artificial Intelligence in Agriculture Sugar beet yield Time-series data Recurrent neural network UAV Meteorological factors |
| title | Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors |
| title_full | Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors |
| title_fullStr | Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors |
| title_full_unstemmed | Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors |
| title_short | Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors |
| title_sort | prediction of sugar beet yield and quality parameters using stacked lstm model with pre harvest uav time series data and meteorological factors |
| topic | Sugar beet yield Time-series data Recurrent neural network UAV Meteorological factors |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000236 |
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