By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant ...By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.展开更多
Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and t...Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and the main aspects of monitoring forest diseases and pests by remote sensing are described, and the application prospect of this technology is forecasted.展开更多
Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research co...Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.展开更多
Risk prediction tools are crucial for population-based management of cardiovascular disease(CVD).However,most prediction models are currently used to assess long-term risk instead of the risk of short-term CVD onset.W...Risk prediction tools are crucial for population-based management of cardiovascular disease(CVD).However,most prediction models are currently used to assess long-term risk instead of the risk of short-term CVD onset.We developed a Dynamic Risk-based Early wAming Monitoring(DREAM)system using large-scale,real-time electronic health record data from 2010 to 2020 from the CHinese Electronic health Records Research in Yinzhou study.The dynamic risk scores were derived from a 1:5 matched nested case-control set comprising 70,470 individuals(11,745 CVD events)and then validated in a cohort of 81,205 individuals(5950 CVD events).The individuals were Chinese adults aged 40-79 years without a history of CVD at baseline.Eleven predictors related to vital signs,laboratory tests,and health service utilization were selected to establish the dynamic scores.The proposed scores were significantly associated with the subsequent CVD onset(adjusted odds ratio,1.21;95%confidence interval,1.20-1.23).The area under the receiver operating characteristic curves(AUCs)was 0.6010(0.5929-0.6092)and 0.6021(0.5937-0.6105)for the long-term 10-year CVD risk<10%and≥10%groups in the derivation set,respectively.In the long-term 10-year CVD risk>10%group in the validation set,the change in AUC in addition to the long-term risk was 0.0235(0.0155-0.0315).By increasing the risk threshold from 7 to 16 points,the proportion of true subsequent CVD cases among those given alerts increased from 40.61%to 85.31%.In terms of management efficiency,the number needed to manage per CVD case ranged from 2.46 to 1.17 using the risk scores.With the increasing popularity and integration of EHR systems with wearable technology,the DREAM scores can be incorporated into an early-warning system and applied in dynamic,real-time,EHR-based,automated management to support healthcare decision making for individuals,general practitioners,and policymakers.展开更多
基金Supported by a Grant from the Science and Technology Project ofYunnan Province(2006NG02)~~
文摘By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.
基金Supported by National Natural Science Foundation of China " Multiagent Simulation and Spatial Prediction of Forest Invasive Alien Species and Diffusion"(30871964)Ministry of Education,New Century Excellent Talents Support Project " Ecological Response Mechanism and Prediction of Spatial Pattern Dynamics of Forest Vegetation"(NCET06-0122)Ministry of Education Innovation Team " Early Warning of Major Forest Pest Disasters and Ecological Control Technology " (IRT0607)~~
文摘Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and the main aspects of monitoring forest diseases and pests by remote sensing are described, and the application prospect of this technology is forecasted.
基金funded by the Zhejiang Agricultural Cooperative and Extensive Project of Key Technology(2020XTTGCY04-02,2020XTTGCY01-05)the Major Special Project for 2025 Scientific and Technological Innovation(Major Scientific and Technological Task Project in Ningbo City)(2021Z048).
文摘Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.
基金supported by the National Natural Science Foundation of China[Grant No.91846112,81973132,81961128006]the Chinese Ministry of Science and Technology[Grant No.2020YFC2003503].
文摘Risk prediction tools are crucial for population-based management of cardiovascular disease(CVD).However,most prediction models are currently used to assess long-term risk instead of the risk of short-term CVD onset.We developed a Dynamic Risk-based Early wAming Monitoring(DREAM)system using large-scale,real-time electronic health record data from 2010 to 2020 from the CHinese Electronic health Records Research in Yinzhou study.The dynamic risk scores were derived from a 1:5 matched nested case-control set comprising 70,470 individuals(11,745 CVD events)and then validated in a cohort of 81,205 individuals(5950 CVD events).The individuals were Chinese adults aged 40-79 years without a history of CVD at baseline.Eleven predictors related to vital signs,laboratory tests,and health service utilization were selected to establish the dynamic scores.The proposed scores were significantly associated with the subsequent CVD onset(adjusted odds ratio,1.21;95%confidence interval,1.20-1.23).The area under the receiver operating characteristic curves(AUCs)was 0.6010(0.5929-0.6092)and 0.6021(0.5937-0.6105)for the long-term 10-year CVD risk<10%and≥10%groups in the derivation set,respectively.In the long-term 10-year CVD risk>10%group in the validation set,the change in AUC in addition to the long-term risk was 0.0235(0.0155-0.0315).By increasing the risk threshold from 7 to 16 points,the proportion of true subsequent CVD cases among those given alerts increased from 40.61%to 85.31%.In terms of management efficiency,the number needed to manage per CVD case ranged from 2.46 to 1.17 using the risk scores.With the increasing popularity and integration of EHR systems with wearable technology,the DREAM scores can be incorporated into an early-warning system and applied in dynamic,real-time,EHR-based,automated management to support healthcare decision making for individuals,general practitioners,and policymakers.
