Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth sta...Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.展开更多
Ecological protection and high-quality development of the Yellow River basin are becoming part of the national strategy in recent years.The Yellow River Estuary has been seriously affected by human activities.Especial...Ecological protection and high-quality development of the Yellow River basin are becoming part of the national strategy in recent years.The Yellow River Estuary has been seriously affected by human activities.Especially,it has been severely polluted by the nitrogen and phosphorus from land sources,which have caused serious eutrophication and harmful algal blooms.Nutrient criteria,however,was not developed for the Yellow River Estuary,which hindered nutrient management measures and eutrophication risk assessment in this key ecological function zone of China.Based on field data during 2004-2019,we adopted the frequency distribution method,correlation analysis,Linear Regression Model(LRM),Classification and Regression Tree(CART)and Nonparametric Changepoint Analysis(nCPA)methods to establish the nutrient criteria for the adjacent waters of Yellow River Estuary.The water quality criteria of dissolved inorganic nitrogen(DIN)and soluble reactive phosphorus(SRP)are recommended as 244.0μg L^(−1) and 22.4μg L^(−1),respectively.It is hoped that the results will provide scientific basis for the formulation of nutrient standards in this important estuary of China.展开更多
基金supported by the National Key Research and Development Program of China(2022YFD2300700)the Open Project Program of State Key Laboratory of Rice Biology,China National Rice Research Institute(20210403)the Zhejiang“Ten Thousand Talents”Plan Science and Technology Innovation Leading Talent Project,China(2020R52035)。
文摘Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.
基金supported by the National Key Research and Development Program of China(No.2018YFC1407601).
文摘Ecological protection and high-quality development of the Yellow River basin are becoming part of the national strategy in recent years.The Yellow River Estuary has been seriously affected by human activities.Especially,it has been severely polluted by the nitrogen and phosphorus from land sources,which have caused serious eutrophication and harmful algal blooms.Nutrient criteria,however,was not developed for the Yellow River Estuary,which hindered nutrient management measures and eutrophication risk assessment in this key ecological function zone of China.Based on field data during 2004-2019,we adopted the frequency distribution method,correlation analysis,Linear Regression Model(LRM),Classification and Regression Tree(CART)and Nonparametric Changepoint Analysis(nCPA)methods to establish the nutrient criteria for the adjacent waters of Yellow River Estuary.The water quality criteria of dissolved inorganic nitrogen(DIN)and soluble reactive phosphorus(SRP)are recommended as 244.0μg L^(−1) and 22.4μg L^(−1),respectively.It is hoped that the results will provide scientific basis for the formulation of nutrient standards in this important estuary of China.
