Recent advances in spectral sensing techniques and machine learning(ML)methods have enabled the estimation of plant physiochemical traits.Nitrogen(N)is a primary limiting factor for terrestrial forest growth,but tradi...Recent advances in spectral sensing techniques and machine learning(ML)methods have enabled the estimation of plant physiochemical traits.Nitrogen(N)is a primary limiting factor for terrestrial forest growth,but traditional methods for N determination are labor-intensive,time-consuming,and destructive.In this study,we present a rapid,non-destructive method to predict leaf N concentration(LNC)in Metasequoia glyptostroboides plantations under N and phosphorus(P)fertilization using ML techniques and unmanned aerial vehicle(UAV)-based RGB(red,green,blue)images.Nine spectral vegetation indices(VIs)were extracted from the RGB images.The spectral reflectance and VIs were used as input features to construct models for estimating LNC based on support vector machine,ran-dom forest(RF),and multiple linear regression,gradient boosting regression and classification and regression trees(CART).The results show that RF is the best fitting model for estimating LNC with a coefficient of determination(R2)of 0.73.Using this model,we evaluated the effects of N and P treatments on LNC and found a significant increase with N and a decrease with P.Height,diameter at breast height(DBH),and crown width of all M.glyptostroboides were analyzed by Pearson correlation with the predicted LNC.DBH was significantly correlated with LNC under N treat-ment.Our results highlight the potential of combining UAV RGB images with an ML algorithm as an efficient,scalable,and cost-effective method for LNC quantification.Future research can extend this approach to different tree species and different plant traits,paving the way for large-scale,time-efficient plant growth monitoring.展开更多
Vapreotide acetate （Vap） was used as a biotemplate to synthesize silver nanocages through direct co-incubation of a AgNO3 solution, following by reduction using fresh NaBH4. The characterized vapreotide-templated si...Vapreotide acetate （Vap） was used as a biotemplate to synthesize silver nanocages through direct co-incubation of a AgNO3 solution, following by reduction using fresh NaBH4. The characterized vapreotide-templated silver nanocages （Vap-AgNCs） presented a wide and red shifted absorption band with a maximum between 480 nm and 800 nm and possessed a uniform structure with a face-centered cubic crystal structure. The biocompatibiliW of Vap-AgNCs was assessed using the MTT method, indicating Vap-AgNCs had better biocompatibility when its concentration was lower than 2,5 × 10-4 mmol. L- 1. The photothermal characteristics of Vap-AgNCs were analyzed with laser irradiation （808 nm, 1,5 W, cm-2） and the results showed that the temperature of the Vap- AgNCs solution reached 45 ℃ starting from 25 ℃ within 5 min. Additionally, Vap-AgNCs with a laser led to HeLa cell death. Therefore, the prepared Vap-AgNCs is expected to be an effective photothermal therapy agent.展开更多
基金supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(2022C02053)National Natural Science Foundation of China(NSFC)(32201632).
文摘Recent advances in spectral sensing techniques and machine learning(ML)methods have enabled the estimation of plant physiochemical traits.Nitrogen(N)is a primary limiting factor for terrestrial forest growth,but traditional methods for N determination are labor-intensive,time-consuming,and destructive.In this study,we present a rapid,non-destructive method to predict leaf N concentration(LNC)in Metasequoia glyptostroboides plantations under N and phosphorus(P)fertilization using ML techniques and unmanned aerial vehicle(UAV)-based RGB(red,green,blue)images.Nine spectral vegetation indices(VIs)were extracted from the RGB images.The spectral reflectance and VIs were used as input features to construct models for estimating LNC based on support vector machine,ran-dom forest(RF),and multiple linear regression,gradient boosting regression and classification and regression trees(CART).The results show that RF is the best fitting model for estimating LNC with a coefficient of determination(R2)of 0.73.Using this model,we evaluated the effects of N and P treatments on LNC and found a significant increase with N and a decrease with P.Height,diameter at breast height(DBH),and crown width of all M.glyptostroboides were analyzed by Pearson correlation with the predicted LNC.DBH was significantly correlated with LNC under N treat-ment.Our results highlight the potential of combining UAV RGB images with an ML algorithm as an efficient,scalable,and cost-effective method for LNC quantification.Future research can extend this approach to different tree species and different plant traits,paving the way for large-scale,time-efficient plant growth monitoring.
基金Supported by the National Natural Science Foundation of China(21476190)Hebei Province Key Basic Research Fund(15961301D)
文摘Vapreotide acetate （Vap） was used as a biotemplate to synthesize silver nanocages through direct co-incubation of a AgNO3 solution, following by reduction using fresh NaBH4. The characterized vapreotide-templated silver nanocages （Vap-AgNCs） presented a wide and red shifted absorption band with a maximum between 480 nm and 800 nm and possessed a uniform structure with a face-centered cubic crystal structure. The biocompatibiliW of Vap-AgNCs was assessed using the MTT method, indicating Vap-AgNCs had better biocompatibility when its concentration was lower than 2,5 × 10-4 mmol. L- 1. The photothermal characteristics of Vap-AgNCs were analyzed with laser irradiation （808 nm, 1,5 W, cm-2） and the results showed that the temperature of the Vap- AgNCs solution reached 45 ℃ starting from 25 ℃ within 5 min. Additionally, Vap-AgNCs with a laser led to HeLa cell death. Therefore, the prepared Vap-AgNCs is expected to be an effective photothermal therapy agent.
