Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and...Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.展开更多
The Nineteenth National Congress of the Communist Party of China has defined the mission of educating people for our educators.The style of study reflects the education philosophy,culture and spirit of a university fr...The Nineteenth National Congress of the Communist Party of China has defined the mission of educating people for our educators.The style of study reflects the education philosophy,culture and spirit of a university from the inside to the outside. Starting from the present situation and existing problems of the construction of the style of study in colleges and universities in the new period,this paper puts forward some recommendations for the construction of the style of study in colleges and universities in the new period.展开更多
Rice plant counting is crucial for many applications in rice production,such as yield estimation,growth diagnosis,disaster loss assessment,etc.Currently,rice counting still heavily relies on tedious and time-consuming...Rice plant counting is crucial for many applications in rice production,such as yield estimation,growth diagnosis,disaster loss assessment,etc.Currently,rice counting still heavily relies on tedious and time-consuming manual operation.To alleviate the workload of rice counting,we employed an UAV(unmanned aerial vehicle)to collect the RGB images of the paddy field.Then,we proposed a new rice plant counting,locating,and sizing method(RiceNet),which consists of one feature extractor frontend and 3 feature decoder modules,namely,density map estimator,plant location detector,and plant size estimator.In RiceNet,rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps.To verify the validity of our method,we propose a new UAV-based rice counting dataset,which contains 355 images and 257,793 manual labeled points.Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2,respectively.Moreover,we validated the performance of our method with two other popular crop datasets.On these three datasets,our method significantly outperforms state-of-the-art methods.Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.展开更多
Topological spin structures of light,including the Skyrmion,Meron,and bi-Meron,are intriguing optical phenomena that arise from spin-orbit coupling.They have promising potential applications in nano-metrology,data sto...Topological spin structures of light,including the Skyrmion,Meron,and bi-Meron,are intriguing optical phenomena that arise from spin-orbit coupling.They have promising potential applications in nano-metrology,data storage,super-resolved imaging and chiral detection.Aside from the electric part of optical spin,of equal importance is the magnetic part,particularly the H-type electromagnetic modes for which the spin topological properties of the field are dominated by the magnetic field.However,their observation and measurement remains absent and faces difficult challenges.Here,we design a unique type of anapole probe to measure specifically the photonic spin structures dominated by magnetic fields.The probe is composed of an Ag-core and Si-shell nanosphere,which manifests as a pure magnetic dipole with no electric response.The effectiveness of the method was validated by characterizing the magnetic field distributions of various focused vector beams.It was subsequently employed to measure the magnetic topological spin structures,including individual Skyrmions and Meron/Skyrmion lattices for the first time.The proposed method may be a powerful tool to characterize the magnetic properties of optical spin and valuable in advancing spin photonics.展开更多
基金supported by the National Natural Science Foundation of China (61701260 and 62271266)the Postgraduate Research&Practice Innovation Program of Jiangsu Province (SJCX21_0255)the Postdoctoral Research Program of Jiangsu Province(2019K287)。
文摘Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.
文摘The Nineteenth National Congress of the Communist Party of China has defined the mission of educating people for our educators.The style of study reflects the education philosophy,culture and spirit of a university from the inside to the outside. Starting from the present situation and existing problems of the construction of the style of study in colleges and universities in the new period,this paper puts forward some recommendations for the construction of the style of study in colleges and universities in the new period.
基金supported in part by the National Natural Science Foundation of China(grant nos.61701260,61876211,and 62271266)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(grant no.SJCX21_0255)the Postdoctoral Research Program of Jiangsu Province(grant no.2019K287).
文摘Rice plant counting is crucial for many applications in rice production,such as yield estimation,growth diagnosis,disaster loss assessment,etc.Currently,rice counting still heavily relies on tedious and time-consuming manual operation.To alleviate the workload of rice counting,we employed an UAV(unmanned aerial vehicle)to collect the RGB images of the paddy field.Then,we proposed a new rice plant counting,locating,and sizing method(RiceNet),which consists of one feature extractor frontend and 3 feature decoder modules,namely,density map estimator,plant location detector,and plant size estimator.In RiceNet,rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps.To verify the validity of our method,we propose a new UAV-based rice counting dataset,which contains 355 images and 257,793 manual labeled points.Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2,respectively.Moreover,we validated the performance of our method with two other popular crop datasets.On these three datasets,our method significantly outperforms state-of-the-art methods.Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.
基金This work was supported by the Guangdong Major Project of Basic Research No.2020B0301030009the National Natural Science Foundation of China Grants U1701661,61935013,62075139,61905163,62205217,12174266,12174310,and 12204309+4 种基金the Leadership of Guangdong Province Program Grant 00201505Natural Science Foundation of Guangdong Province Grant 2016A030312010Science and Technology Innovation Commission of Shenzhen Grants RCJC20200714114435063 and JCYJ20200109114018750Shenzhen Peacock Plan Grants KQTD2015071016560101 and KQTD20170330110444030China Postdoctoral Science Foundation(Grant No.2019M663048).
文摘Topological spin structures of light,including the Skyrmion,Meron,and bi-Meron,are intriguing optical phenomena that arise from spin-orbit coupling.They have promising potential applications in nano-metrology,data storage,super-resolved imaging and chiral detection.Aside from the electric part of optical spin,of equal importance is the magnetic part,particularly the H-type electromagnetic modes for which the spin topological properties of the field are dominated by the magnetic field.However,their observation and measurement remains absent and faces difficult challenges.Here,we design a unique type of anapole probe to measure specifically the photonic spin structures dominated by magnetic fields.The probe is composed of an Ag-core and Si-shell nanosphere,which manifests as a pure magnetic dipole with no electric response.The effectiveness of the method was validated by characterizing the magnetic field distributions of various focused vector beams.It was subsequently employed to measure the magnetic topological spin structures,including individual Skyrmions and Meron/Skyrmion lattices for the first time.The proposed method may be a powerful tool to characterize the magnetic properties of optical spin and valuable in advancing spin photonics.
