Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.Howe...Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.展开更多
To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA...To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA)and the Convolutional Block Attention Module(CBAM)—to enhance detection accuracy.Additionally,Shape-IoU is employed as the loss function to refine localization precision.Our model further incorporates an adaptive feature fusion mechanism,which optimizes multi-scale object representation,ensuring robust tracking in complex aerial environments.We evaluate the performance of AVA-PRB on two benchmark datasets:Aerial Person Detection and VisDrone2019-Det.The model achieves 60.9%mAP@0.5 on the Aerial Person Detection dataset,and 51.2%mAP@0.5 on VisDrone2019-Det,demonstrating its effectiveness in aerial object detection.Beyond detection,we propose a novel trajectory estimation method that improves movement path prediction under aerial motion.Experimental results indicate that our approach reduces path deviation by up to 64%,effectively mitigating errors caused by rapid camera movements and background variations.By optimizing feature extraction and enhancing spatialtemporal coherence,our method significantly improves object tracking under aerial moving perspectives.This research addresses the limitations of fixed-camera tracking,enhancing flexibility and accuracy in aerial tracking applications.The proposed approach has broad potential for real-world applications,including surveillance,traffic monitoring,and environmental observation.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
Unmanned Aerial Vehicles (UAVs) have emerged as innovative tools in agriculture, revolutionizing crop protection practices and the use of pesticide combinations to aid the management of insect pests and diseases in a ...Unmanned Aerial Vehicles (UAVs) have emerged as innovative tools in agriculture, revolutionizing crop protection practices and the use of pesticide combinations to aid the management of insect pests and diseases in a single application. This research delves into assessing the efficacy of drone-based pesticide spraying utilizing combinations of pesticides to combat insect pests and diseases in rice cultivation. In kharif 2022, the physically compatible combination of insecticides (chlorantraniliprole 18.5% SC and tetraniliprole 200 SC) with fungicides (picoxystrobin 7.5%+tricyclazole 22.5% SC and tebuconazole 50%+trifloxystrobin 25% WG) were administered via drones and compared with conventional Taiwan sprayer. The results indicated that tebuconazole+trifloxystrobin, when applied via drones, exhibited the highest control efficacy against the brown spot, sheath blight, and sheath rot (47.8%, 77.4%, and 75.2% respectively). Moreover, combination treatment, i.e., tetraniliprole+(tebuconazole+trifloxystrobin), applied using a drone, achieved the most effective control (78.1%) against grain discoloration. Additionally, drone-based tetraniliprole application showed effectiveness against stem borer and whorl maggot (efficacy rates of 49.1%, 66.6%, and 60.7% for dead hearts, white ear, and whorl maggot, respectively). Overall, the pesticide combination treatment, i.e., tetraniliprole+(tebuconazole+trifloxystrobin), showed higher control efficacy against all the insect pests and diseases and recorded the highest grain yield of 7995 kg/hm2 with an incremental cost-benefit ratio (ICBR) of (1:5.63) when sprayed with a drone. Overall, this study underscores the potential of drone-assisted pesticide application in effectively managing multiple insect pests and diseases in rice, offering superior precision, efficacy, efficiency, and yield.展开更多
基金the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300100)the National Key Research and Development Program of China(Grant Nos.2023YFB3809600 and 2023YFC3007801)+1 种基金the National Natural Science Foundation of China(Grant Nos.62301543 and U24A20320)the Shanghai Sailing Program(Grant No.21YF1455700).
文摘Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.
基金funded by theNational Science and TechnologyCouncil(NSTC),Taiwan,under grant numbers NSTC 113-2634-F-A49-007 and NSTC 112-2634-F-A49-007.
文摘To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA)and the Convolutional Block Attention Module(CBAM)—to enhance detection accuracy.Additionally,Shape-IoU is employed as the loss function to refine localization precision.Our model further incorporates an adaptive feature fusion mechanism,which optimizes multi-scale object representation,ensuring robust tracking in complex aerial environments.We evaluate the performance of AVA-PRB on two benchmark datasets:Aerial Person Detection and VisDrone2019-Det.The model achieves 60.9%mAP@0.5 on the Aerial Person Detection dataset,and 51.2%mAP@0.5 on VisDrone2019-Det,demonstrating its effectiveness in aerial object detection.Beyond detection,we propose a novel trajectory estimation method that improves movement path prediction under aerial motion.Experimental results indicate that our approach reduces path deviation by up to 64%,effectively mitigating errors caused by rapid camera movements and background variations.By optimizing feature extraction and enhancing spatialtemporal coherence,our method significantly improves object tracking under aerial moving perspectives.This research addresses the limitations of fixed-camera tracking,enhancing flexibility and accuracy in aerial tracking applications.The proposed approach has broad potential for real-world applications,including surveillance,traffic monitoring,and environmental observation.
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.
文摘Unmanned Aerial Vehicles (UAVs) have emerged as innovative tools in agriculture, revolutionizing crop protection practices and the use of pesticide combinations to aid the management of insect pests and diseases in a single application. This research delves into assessing the efficacy of drone-based pesticide spraying utilizing combinations of pesticides to combat insect pests and diseases in rice cultivation. In kharif 2022, the physically compatible combination of insecticides (chlorantraniliprole 18.5% SC and tetraniliprole 200 SC) with fungicides (picoxystrobin 7.5%+tricyclazole 22.5% SC and tebuconazole 50%+trifloxystrobin 25% WG) were administered via drones and compared with conventional Taiwan sprayer. The results indicated that tebuconazole+trifloxystrobin, when applied via drones, exhibited the highest control efficacy against the brown spot, sheath blight, and sheath rot (47.8%, 77.4%, and 75.2% respectively). Moreover, combination treatment, i.e., tetraniliprole+(tebuconazole+trifloxystrobin), applied using a drone, achieved the most effective control (78.1%) against grain discoloration. Additionally, drone-based tetraniliprole application showed effectiveness against stem borer and whorl maggot (efficacy rates of 49.1%, 66.6%, and 60.7% for dead hearts, white ear, and whorl maggot, respectively). Overall, the pesticide combination treatment, i.e., tetraniliprole+(tebuconazole+trifloxystrobin), showed higher control efficacy against all the insect pests and diseases and recorded the highest grain yield of 7995 kg/hm2 with an incremental cost-benefit ratio (ICBR) of (1:5.63) when sprayed with a drone. Overall, this study underscores the potential of drone-assisted pesticide application in effectively managing multiple insect pests and diseases in rice, offering superior precision, efficacy, efficiency, and yield.
