[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective ...A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.展开更多
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
基金Projects(50974039,50634030)supported by the National Natural Science Foundation of China
文摘A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.
