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 proc...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 R^(2)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.展开更多
Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of maize.Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agricultu...Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of maize.Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture,based on unmanned aerial vehicle(UAV)remote sensing technology.In this study,the hyperspectral images were acquired by UAV and the leaf nitrogen content(LNC)and leaf nitrogen accumulation(LNA)were measured to estimate the N nutrition status of maize.24 vegetation indices(VIs)were constructed using hyperspectral images,and four prediction models were used to estimate the LNC and LNA of maize.The models include a single linear regression model,multivariable linear regression(MLR)model,random forest regression(RFR)model,and support vector regression(SVR)model.Moreover,the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields.The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll(NDchl)had the highest prediction accuracy for LNC(R^(2),RMSE,and RE were 0.72,0.21,and 12.19%,respectively)and LNA(R^(2),RMSE,and RE were 0.77,0.26,and 14.34%,respectively).And then,24 VIs were divided into 13 important VIs and 11 unimportant VIs.Three prediction models for LNC and LNA were constructed using 13 important VIs,and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model,in which RFR model had the highest prediction accuracy for the validation dataset of LNC(R^(2),RMSE,and RE were 0.78,0.16,and 8.83%,respectively)and LNA(R^(2),RMSE,and RE were 0.85,0.19,and 9.88%,respectively).This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.展开更多
基金supported by the Science and Technology projects Inner Mongolia(Grant No.2019ZD024)National Center of Pratacultural Technology Innovation(under preparation)Special fund for innovation platform construction(CCPTZX2023K03).
文摘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 R^(2)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.
基金financially supported by the Hainan Province Science and Technology Special Fund(Grant No.ZDYF2021GXJS038 and Grant No.ZDYF2024XDNY196)Hainan Provincial Natural Science Foundation of China(Grant No.320RC486)the National Natural Science Foundation of China(Grant No.42167011).
文摘Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of maize.Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture,based on unmanned aerial vehicle(UAV)remote sensing technology.In this study,the hyperspectral images were acquired by UAV and the leaf nitrogen content(LNC)and leaf nitrogen accumulation(LNA)were measured to estimate the N nutrition status of maize.24 vegetation indices(VIs)were constructed using hyperspectral images,and four prediction models were used to estimate the LNC and LNA of maize.The models include a single linear regression model,multivariable linear regression(MLR)model,random forest regression(RFR)model,and support vector regression(SVR)model.Moreover,the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields.The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll(NDchl)had the highest prediction accuracy for LNC(R^(2),RMSE,and RE were 0.72,0.21,and 12.19%,respectively)and LNA(R^(2),RMSE,and RE were 0.77,0.26,and 14.34%,respectively).And then,24 VIs were divided into 13 important VIs and 11 unimportant VIs.Three prediction models for LNC and LNA were constructed using 13 important VIs,and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model,in which RFR model had the highest prediction accuracy for the validation dataset of LNC(R^(2),RMSE,and RE were 0.78,0.16,and 8.83%,respectively)and LNA(R^(2),RMSE,and RE were 0.85,0.19,and 9.88%,respectively).This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.
