Visible/near-infrared(Vis/NIR)spectroscopy technology has been extensively utilized for the determination of soluble solids content(SSC)in fruits.Nonetheless,the spectral distortion resulting from temperature variatio...Visible/near-infrared(Vis/NIR)spectroscopy technology has been extensively utilized for the determination of soluble solids content(SSC)in fruits.Nonetheless,the spectral distortion resulting from temperature variations in the sample leads to a decrease in detection accuracy.To mitigate the influence of temperature fluctuations on the accuracy of SSC detection in fruits,using watermelon as an example,this study presents a knowledge-guided temperature correction method utilizing one-dimensional convolutional neural networks(1D-CNN).This method consists of two stages:the first stage involves utilizing 1D-CNN models and gradient-weighted class activation mapping(Grad-CAM)method to acquire gradient-weighted features correlating with temperature.The second stage involves mapping these features and integrating them with the original Vis/NIR spectrum,and then train and test the partial least squares(PLS)model.This knowledge-guided method can identify wavelength bands with high temperature correlation in the Vis/NIR spectra,offering valuable guidance for spectral data processing.The performance of the PLS model constructed using the 15℃ spectrum guided by this method is superior to that of the global model,and can reduce the root mean square error of the prediction set(RMSEP)to 0.324°Brix,which is 32.5%lower than the RMSEP of the global model(0.480°Brix).The method proposed in this study has superior temperature correction effects than slope and bias correction,piecewise direct standardization,and external parameter orthogonalization correction methods.The results indicate that the knowledge-guided temperature correction method based on deep learning can significantly enhance the detection accuracy of SSC in watermelon,providing valuable reference for the development of PLS calibration methods.展开更多
In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges whe...In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges when datasets lack the comprehensive information necessary for addressing complex scenarios,which hampers adaptability.Thus,enhancing data completeness is essential.Knowledge-guided virtual sample generation transforms domain knowledge into extensive virtual datasets,thereby reducing dependence on limited real samples and enabling zero-sample fault diagnosis.This study used building air conditioning systems as a case study.We innovatively used the large language model(LLM)to acquire domain knowledge for sample generation,significantly lowering knowledge acquisition costs and establishing a generalized framework for knowledge acquisition in engineering applications.This acquired knowledge guided the design of diffusion boundaries in mega-trend diffusion(MTD),while the Monte Carlo method was used to sample within the diffusion function to create information-rich virtual samples.Additionally,a noise-adding technique was introduced to enhance the information entropy of these samples,thereby improving the robustness of neural networks trained with them.Experimental results showed that training the diagnostic model exclusively with virtual samples achieved an accuracy of 72.80%,significantly surpassing traditional small-sample supervised learning in terms of generalization.This underscores the quality and completeness of the generated virtual samples.展开更多
Accurate and real-time monitoring true leaf area index(LAI)is an essential for assessing crop growth status and predicting yields.Conventional LAI inversion approaches have been constrained by insufficient data repres...Accurate and real-time monitoring true leaf area index(LAI)is an essential for assessing crop growth status and predicting yields.Conventional LAI inversion approaches have been constrained by insufficient data represen-tativeness and environmental variability,particularly when applied across interannual variations and different phenological stages.This study presented a novel methodology integrating three-dimensional radiative transfer modeling(3D RTM)with knowledge-guided deep learning to address these limitations.We developed a knowledge-guided convolutional neural network(KGCNN)architecture incorporating 3D canopy structural physics,enhanced through transfer learning(TL)techniques for cross-temporal adaptation.The KGCNN model was initially pre-trained on synthetic datasets generated by the large-scale remote sensing scattering model(LESS),followed by domain-specific fine-tuning using 2021 field measurements,and culminating in cross-year validation with 2022-2023 datasets.Our results demonstrated significant improvements over conventional ap-proaches,with the 3D RTM-based KGCNN achieving superior performance compared to 1D RTM implementations(PROSAIL+CNN+TL).Specially,for the 2022 dataset,the overall R^(2) increased by 0.27 and RMSE decreased by 2.46;for the 2023 dataset,the overall RMSE decreased by 1.62,compared to the PROSAIL+TL method.Our method(3D RTM+KGCNN+TL)delivered superior LAI retrieval accuracy on the two-year datasets compared to LSTM+TL,RNN+TL,and 3D RTM+RF models.This study also introduced an effective 3D scene modeling strategy that integrates scenarios representing the measured data range with additional synthetic scenes gener-ated through random combinations of structural parameters.By incorporating detailed 3D crop structural in-formation into the KGCNN network and fine-tuning the model with measured data,the approach significantly enhanced the model's adaptability to varying data distributions across different years and growth stages.This approach thus improved both the accuracy and stability of true LAI retrieval.展开更多
基金The Key Research and Development of Xinjiang Uygur Autonomous Region(Grant No.2022B02049-1).
文摘Visible/near-infrared(Vis/NIR)spectroscopy technology has been extensively utilized for the determination of soluble solids content(SSC)in fruits.Nonetheless,the spectral distortion resulting from temperature variations in the sample leads to a decrease in detection accuracy.To mitigate the influence of temperature fluctuations on the accuracy of SSC detection in fruits,using watermelon as an example,this study presents a knowledge-guided temperature correction method utilizing one-dimensional convolutional neural networks(1D-CNN).This method consists of two stages:the first stage involves utilizing 1D-CNN models and gradient-weighted class activation mapping(Grad-CAM)method to acquire gradient-weighted features correlating with temperature.The second stage involves mapping these features and integrating them with the original Vis/NIR spectrum,and then train and test the partial least squares(PLS)model.This knowledge-guided method can identify wavelength bands with high temperature correlation in the Vis/NIR spectra,offering valuable guidance for spectral data processing.The performance of the PLS model constructed using the 15℃ spectrum guided by this method is superior to that of the global model,and can reduce the root mean square error of the prediction set(RMSEP)to 0.324°Brix,which is 32.5%lower than the RMSEP of the global model(0.480°Brix).The method proposed in this study has superior temperature correction effects than slope and bias correction,piecewise direct standardization,and external parameter orthogonalization correction methods.The results indicate that the knowledge-guided temperature correction method based on deep learning can significantly enhance the detection accuracy of SSC in watermelon,providing valuable reference for the development of PLS calibration methods.
基金supported by the National Natural Science Foundation of China(No.62306281)the Natural Science Foundation of Zhejiang Province(Nos.LQ23E060006 and LTGG24E050005)the Key Research Plan of Jiaxing City(No.2024BZ20016).
文摘In the era of big data,data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making.However,the challenge of“small samples in big data”emerges when datasets lack the comprehensive information necessary for addressing complex scenarios,which hampers adaptability.Thus,enhancing data completeness is essential.Knowledge-guided virtual sample generation transforms domain knowledge into extensive virtual datasets,thereby reducing dependence on limited real samples and enabling zero-sample fault diagnosis.This study used building air conditioning systems as a case study.We innovatively used the large language model(LLM)to acquire domain knowledge for sample generation,significantly lowering knowledge acquisition costs and establishing a generalized framework for knowledge acquisition in engineering applications.This acquired knowledge guided the design of diffusion boundaries in mega-trend diffusion(MTD),while the Monte Carlo method was used to sample within the diffusion function to create information-rich virtual samples.Additionally,a noise-adding technique was introduced to enhance the information entropy of these samples,thereby improving the robustness of neural networks trained with them.Experimental results showed that training the diagnostic model exclusively with virtual samples achieved an accuracy of 72.80%,significantly surpassing traditional small-sample supervised learning in terms of generalization.This underscores the quality and completeness of the generated virtual samples.
基金supported by the National Key Research and Development Program of China(2021YFD2000102)the Natural Science Foundation of China(42371373)the Special Fund for Construction of Scientific and Technological Innovation Ability of Beijing Academy of Agriculture and Forestry Sciences(KJCX20230434).
文摘Accurate and real-time monitoring true leaf area index(LAI)is an essential for assessing crop growth status and predicting yields.Conventional LAI inversion approaches have been constrained by insufficient data represen-tativeness and environmental variability,particularly when applied across interannual variations and different phenological stages.This study presented a novel methodology integrating three-dimensional radiative transfer modeling(3D RTM)with knowledge-guided deep learning to address these limitations.We developed a knowledge-guided convolutional neural network(KGCNN)architecture incorporating 3D canopy structural physics,enhanced through transfer learning(TL)techniques for cross-temporal adaptation.The KGCNN model was initially pre-trained on synthetic datasets generated by the large-scale remote sensing scattering model(LESS),followed by domain-specific fine-tuning using 2021 field measurements,and culminating in cross-year validation with 2022-2023 datasets.Our results demonstrated significant improvements over conventional ap-proaches,with the 3D RTM-based KGCNN achieving superior performance compared to 1D RTM implementations(PROSAIL+CNN+TL).Specially,for the 2022 dataset,the overall R^(2) increased by 0.27 and RMSE decreased by 2.46;for the 2023 dataset,the overall RMSE decreased by 1.62,compared to the PROSAIL+TL method.Our method(3D RTM+KGCNN+TL)delivered superior LAI retrieval accuracy on the two-year datasets compared to LSTM+TL,RNN+TL,and 3D RTM+RF models.This study also introduced an effective 3D scene modeling strategy that integrates scenarios representing the measured data range with additional synthetic scenes gener-ated through random combinations of structural parameters.By incorporating detailed 3D crop structural in-formation into the KGCNN network and fine-tuning the model with measured data,the approach significantly enhanced the model's adaptability to varying data distributions across different years and growth stages.This approach thus improved both the accuracy and stability of true LAI retrieval.
