Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and...Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.展开更多
Epileptic seizures are known for their unpredictable nature.However,recent research provides that the transition to seizure event is not random but the result of evidence accumulations.Therefore,a reliable method capa...Epileptic seizures are known for their unpredictable nature.However,recent research provides that the transition to seizure event is not random but the result of evidence accumulations.Therefore,a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients.Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes,spikes,and the amplitude.In this study,spike rate is used as the indicator to anticipate seizures in electroencephalogram(EEG) signal.Spikes detection step is used in EEG signal during interictal,preictal,and ictal periods followed by a mean filter to smooth the spike number.The maximum spike rate in interictal periods is used as an indicator to predict seizures.When the spike number in the preictal period exceeds the threshold,an alarm is triggered.Using the CHB-MIT database,the proposed approach has ensured92% accuracy in seizure prediction for all patients.展开更多
Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSRS) that transforms the automatic identificat...Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSRS) that transforms the automatic identification of multiple developmental stages of rice spikes into the detection of rice spikes of diverse maturity levels. The scales vary greatly in different growth and development stages because rice spikes are dense and small, posing challenges for their effective and accurate detection. We describe a rice spike detection model based on an improved faster regions with convolutional neural network(Faster R-CNN).The model incorporates the following optimization strategies: first, Inception_Res Net-v2 replaces VGG16 as a feature extraction network;second, a feature pyramid network(FPN) replaces single-scale feature maps to fuse with region proposal network(RPN);third, region of interest(Ro I) alignment replaces Ro I pooling, and distance-intersection over union(DIo U) is used as a standard for non-maximum suppression(NMS). The performance of the proposed model was compared with that of the original Faster R-CNN and YOLOv4 models. The mean average precision(m AP) of the rice spike detection model was92.47%, a substantial improvement on the original Faster R-CNN model(with 40.96% m AP) and 3.4%higher than that of the YOLOv4 model, experimentally indicating that the model is more accurate and reliable. The identification results of the model for the heading–flowering, milky maturity, and full maturity stages were within two days of the results of manual observation, fully meeting the needs of agricultural activities.展开更多
基金funded by Jilin Province Innovation and Entrepreneurship Talent Project,grant number 2023QN15funded by Science and Technology Development Plan Project of Jilin Province,grant number 20220202035NC.
文摘Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.
文摘Epileptic seizures are known for their unpredictable nature.However,recent research provides that the transition to seizure event is not random but the result of evidence accumulations.Therefore,a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients.Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes,spikes,and the amplitude.In this study,spike rate is used as the indicator to anticipate seizures in electroencephalogram(EEG) signal.Spikes detection step is used in EEG signal during interictal,preictal,and ictal periods followed by a mean filter to smooth the spike number.The maximum spike rate in interictal periods is used as an indicator to predict seizures.When the spike number in the preictal period exceeds the threshold,an alarm is triggered.Using the CHB-MIT database,the proposed approach has ensured92% accuracy in seizure prediction for all patients.
基金supported by the Key-Area Research and Development Program of Guangdong Province (2019B020214005)Agricultural Research Project and Agricultural Technology Promotion Project of Guangdong (2021KJ383)。
文摘Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSRS) that transforms the automatic identification of multiple developmental stages of rice spikes into the detection of rice spikes of diverse maturity levels. The scales vary greatly in different growth and development stages because rice spikes are dense and small, posing challenges for their effective and accurate detection. We describe a rice spike detection model based on an improved faster regions with convolutional neural network(Faster R-CNN).The model incorporates the following optimization strategies: first, Inception_Res Net-v2 replaces VGG16 as a feature extraction network;second, a feature pyramid network(FPN) replaces single-scale feature maps to fuse with region proposal network(RPN);third, region of interest(Ro I) alignment replaces Ro I pooling, and distance-intersection over union(DIo U) is used as a standard for non-maximum suppression(NMS). The performance of the proposed model was compared with that of the original Faster R-CNN and YOLOv4 models. The mean average precision(m AP) of the rice spike detection model was92.47%, a substantial improvement on the original Faster R-CNN model(with 40.96% m AP) and 3.4%higher than that of the YOLOv4 model, experimentally indicating that the model is more accurate and reliable. The identification results of the model for the heading–flowering, milky maturity, and full maturity stages were within two days of the results of manual observation, fully meeting the needs of agricultural activities.
