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
In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications o...In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications of deep learning methods in a range of real-life domains and to show the practical utility of these techniques.A particular attention goes towards methods to process network data that is capable of modelling complex artificial and natural systems as the interactions of a multitude of simpler entities.展开更多
A method of on-line pretreatment coupled to HPLC with fluorescence detection was developed and validated for the determination of nine quinolones in honey samples. This method simplified the complicated process of sam...A method of on-line pretreatment coupled to HPLC with fluorescence detection was developed and validated for the determination of nine quinolones in honey samples. This method simplified the complicated process of sample pretreatment and reduced sample treatment time. Recovery of the quinolones was between 92% - 101% for spiked honey samples. The limit of detection was 0.22 - 3.78 ng/mL (signal/noise ratio = 3). There was good linear correlation between HPLC peak area and concentration of the quinolones, with a linear range of 1.0 - 100.0 ng/mL and correlation coefficients >0.9997. The method proposed was validated for detecting quinolones in honey samples.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
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In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications of deep learning methods in a range of real-life domains and to show the practical utility of these techniques.A particular attention goes towards methods to process network data that is capable of modelling complex artificial and natural systems as the interactions of a multitude of simpler entities.
文摘A method of on-line pretreatment coupled to HPLC with fluorescence detection was developed and validated for the determination of nine quinolones in honey samples. This method simplified the complicated process of sample pretreatment and reduced sample treatment time. Recovery of the quinolones was between 92% - 101% for spiked honey samples. The limit of detection was 0.22 - 3.78 ng/mL (signal/noise ratio = 3). There was good linear correlation between HPLC peak area and concentration of the quinolones, with a linear range of 1.0 - 100.0 ng/mL and correlation coefficients >0.9997. The method proposed was validated for detecting quinolones in honey samples.
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.
