[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
提出一种改进的初轨确定算法,基于动态阈值的距离搜索方法,以改进传统算法在处理数据时初轨成功率和初轨误差。通过动态调整搜索阈值,旨在实现更精准和高效的初轨确定,以满足当前对空间目标初轨确定的需求;利用LEO,MEO和GEO目标的实测...提出一种改进的初轨确定算法,基于动态阈值的距离搜索方法,以改进传统算法在处理数据时初轨成功率和初轨误差。通过动态调整搜索阈值,旨在实现更精准和高效的初轨确定,以满足当前对空间目标初轨确定的需求;利用LEO,MEO和GEO目标的实测角度数据开展算法测试。介绍了基于动态阈值的距离搜索算法的实现过程,基于数据处理的经验,用动态阈值实现初轨参数质量控制环节的轨道筛选。给出了详细的算法实现流程。利用TLE(Two Line El⁃ements)评估了初轨确定参数误差。基于“烛龙”观测网的中低轨目标和中国科学院长春人造卫星观测站的高轨目标的实测角度数据,开展算法测试。结果表明:LEO,MEO和GEO目标短弧初轨确定成功率分别约为94%,75%和89%,半长轴误差均值分别约为9,12和50 km。该算法适用性强、成功率高、定轨精度高,证明了监测数据的质量。展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
文摘提出一种改进的初轨确定算法,基于动态阈值的距离搜索方法,以改进传统算法在处理数据时初轨成功率和初轨误差。通过动态调整搜索阈值,旨在实现更精准和高效的初轨确定,以满足当前对空间目标初轨确定的需求;利用LEO,MEO和GEO目标的实测角度数据开展算法测试。介绍了基于动态阈值的距离搜索算法的实现过程,基于数据处理的经验,用动态阈值实现初轨参数质量控制环节的轨道筛选。给出了详细的算法实现流程。利用TLE(Two Line El⁃ements)评估了初轨确定参数误差。基于“烛龙”观测网的中低轨目标和中国科学院长春人造卫星观测站的高轨目标的实测角度数据,开展算法测试。结果表明:LEO,MEO和GEO目标短弧初轨确定成功率分别约为94%,75%和89%,半长轴误差均值分别约为9,12和50 km。该算法适用性强、成功率高、定轨精度高,证明了监测数据的质量。
