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Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT

Information Processing in Agriculture 2023年第2期10卷 240-255页

作者： Wenxia Bao Ze Lin Gensheng Hu Dong Liang Linsheng Huang Xin Zhang National Engineering Research Center for Agro-ecological Big Data Analysis and Application Anhui UniversityHefei 230601China

Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield *** frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine *** based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat *** order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature ***,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the *** number of wheat ears in the image can be accurately *** show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.

关键词： Wheat ear counting Frequency domain decomposition Multiscale support value filter Improved sampled contourlet transform Image segmentation Morphological processing

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A Diversion Path Planning Method of Waypoint Based on Flight Traffic Conduction 6

A Diversion Path Planning Method of Waypoint Based on Flight...

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6th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Shi, Hongfang Le, Ningning Miao, Jiahe Li, Qian China Academy of Civil Aviation Science and Technology Big Data Analysis and Application Center Beijing China

ISBN: (纸本)9798350389418

Based on flight operation data, this paper constructs a diversion path planning method for busy waypoints by analyzing the relationship of flight traffic conduction between waypoints. Taking busy waypoint KHN as an example, a total of two diversion paths are planned, specifically one in the east-west direction and one in the north-south direction, and the two diversion paths are evaluated in terms of safety and economy, and the results of the evaluations are within the acceptable range. The method of this paper can improve the capacity of airspace resources without changing the existing air route network;at the same time, this method can also assist in identifying the bottleneck of airspace resources, which means identifying the key waypoints affecting the flow of diversion paths, so as to provide certain theoretical references for China to enhance the total number of flights, and to improve the utilization rate of airspace resources and operational efficiency. © 2024 IEEE.

关键词： Flight paths

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A Sentinel-2-based classification algorithm of algal blooms and aquatic vegetation 5

A Sentinel-2-based classification algorithm of algal blooms ...

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5th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2024

作者： Wang, Kang Huang, Linsheng Ni, Guigao Ruan, Chao National Engineering Research Center for Agro-Ecological Big Data Analysis& Application Anhui University Hefei China

ISBN: (纸本)9781510681712

Floating/emergent aquatic vegetation (FEAV) and algal blooms (AB) play different roles in regulating lake ecosystems. High spatiotemporal information is crucial for assessing lake status. However, distinguishing between AB and FEAV using satellite remote sensing poses challenges due to their similar spectral characteristics. To address this, we propose an automatic classification algorithm (BF algorithm), incorporating multiple spectral features, based on Sentinel-2 imagery. Validation in Lake Taihu yielded an average overall classification accuracy of 93.00% for the BF algorithm. Subsequently, the algorithm was successfully applied for long-term monitoring (2019-2022) of AB and FEAV in Lake Taihu. This algorithm holds prospects for long-term monitoring in small to medium-sized eutrophic lakes. © 2024 SPIE.

关键词： Remote sensing

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Design and application of a Traffic Light System at Airport Level Crossing Based on Video Surveillance Technology 5

Design and Application of a Traffic Light System at Airport ...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Miao, Jiahe Le, Ningning Zhang, Xinyue Li, Qian Shi, Hongfang Li, Yang China Academy of Civil Aviation Science & Technology Big Data Analysis and Application Center Beijing China

ISBN: (纸本)9798350310603

This paper presents a design scheme of traffic light system at airport level crossing based on video surveillance technology. The system uses cameras and edge computing to detect the aircraft about to pass through the intersection with signals transmitted through data radio stations, and controls the traffic lights to alert vehicle drivers, to reduce the risk of conflict or collision between vehicles and the aircraft. The overall framework of the system and the design scheme of each functional module are introduced in detail, and the practical application of the system at a domestic airport is shown in the end. The system has the characteristics of low cost, easy deployment, high reliability, low delay, and strong expansibility, which can provide help for the safety management of airport surface operation. © 2023 IEEE.

关键词： Object detection

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High accuracy phase retrieval algorithm based on the transport of intensity equation under multi wavelength

High accuracy phase retrieval algorithm based on the transpo...

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2022 Conference on Infrared, Millimeter, Terahertz Waves and applications, IMT 2022

作者： Cheng, Hong Wu, Wentong Zhang, Quanbing Sun, Siwei Cheng, Yifan National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China

ISBN: (纸本)9781510662476

A high-precision phase retrieval algorithm based on the transport of intensity equation for multi wavelength illumination is proposed. First of all, the traditional transport of intensity equation method is used to obtain the phase of a single wavelength, and the initial phase of different wavelengths is calculated according to the corresponding phase relationship between different wavelengths. Secondly, through the wavelength phase synthesis module, the phase of the object under multiple wavelengths is obtained. For the noise amplification problem introduced in the phase synthesis step, the phase unwrapping module is used to eliminate the noise level in the initial phase result and gradually reduce it to the noise level of two synthetic short wavelengths. Finally, the high-precision phase retrieval result is obtained. The experimental results verify the correctness and effectiveness of the proposed method. © 2023 SPIE.

关键词： Optical engineering

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Extraction of soybean planting areas based on multi-temporal Sentinel-1/2 data 3

Extraction of soybean planting areas based on multi-temporal...

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3rd International Conference on Computer Vision and Pattern analysis, ICCPA 2023

作者： Ding, Han Huang, Linsheng National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China

ISBN: (纸本)9781510667563

Accurate knowledge of soybean distribution is essential for regulating the soybean market and promoting sustainable development. This research presents a method for identifying soybean planting areas using multispectral data and feature selection models. The study area was the county of Guoyang, Anhui Province. Sentinel-1/2 data from three different time periods were used to calculate vegetation indices, textures, and other features. Initially, non-cropland elements in the study area were gradually masked by constructing decision trees. Then ReliefF algorithm was combined with Random Forest, Support Vector Machine, and BP Neural Network to develop three classification models: ReliefF-RF, ReliefF-SVM, and ReliefF-BPNN, and the best feature subsets were selected as input for each of the three models, an overall distribution map of soybeans in the study area was obtained. Finally, the performance of three models was evaluated by utilizing the distribution results of soybeans obtained from the classification of Planet images and seven uniformly distributed sample plots (500m × 500m) within the study area. The results showed that the Kappa coefficient was between 0.73 to 0.85, and the overall accuracy ranged from 87.42% to 93.22%. The ReliefF-RF model had the best performance in the experiment. © 2023 SPIE.

关键词： Decision trees

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Optimizing Resource Allocation in the Internet of Vehicles: An Intelligent Vehicle-Edge-Cloud Collaboration Approach 20th

Optimizing Resource Allocation in the Internet of Vehicle...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Chen, Weijie Lin, Li Xiong, Jinbo Lin, Jiayi Huang, Ruihong Wang, Xing College of Computer and Cyber Security Fujian Normal University Fuzhou China Fujian Provincial University Engineering Research Center of Big Data Analysis and Application Fuzhou China

ISBN: (纸本)9789819628636

The proliferation of the Internet of Vehicles (IoV) has spurred the development of computationally intensive and latency-sensitive applications in vehicles. However, limited onboard computing resources hinder their execution. Vehicle edge computing addresses this by providing readily accessible resources from edge servers. However, efficient resource allocation remains a challenge due to limited edge server capacity and dynamic traffic patterns. This paper proposes a novel three-layer collaborative intelligence framework that leverages edge and cloud resources to minimize system latency in IoV. We employ a deep reinforcement learning algorithm to adapt to dynamic traffic conditions and make optimal resource allocation decisions. Extensive experiments demonstrate a significant reduction in latency (26.75%) compared to baseline methods. © IFIP International Federation for Information Processing 2025.

关键词： Deep reinforcement learning

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Detection of Densely Aggregated Wheat Rust Spores in Complex Background Microscopic Images 5

Detection of Densely Aggregated Wheat Rust Spores in Complex...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Lu, Feiran Lei, Yu Liang, Dong Chen, Xu Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Hefei China Anhui University School of Internet Hefei China

ISBN: (纸本)9798350373820

To solve the issue of inaccurate detection of summer spores of wheat stripe rust due to complex backgrounds, small targets, and dense adherence among individual spores, an improved model based on YOLOv7 is proposed. This work involves creating a summer spore sample dataset to establish a complex environmental data set for simulated collection. The addition of the CBAM attention mechanism helps elevate the model's precision in detecting spores. Moreover, the ASFF adaptive feature fusion mechanism has been improved to preserve more information of small target features, thus reducing omissions. The introduction of the Wise-IoU loss function enhances the quality of detection anchor boxes and improves the model's localization performance. Trials indicate that the revised model has achieved an mean average precision (mAP) of 98.1% at the IOU threshold of 0.5. Compared to other models, both the average accuracy of detection and the speed of detection have been significantly boosted. The outcomes demonstrate that the enhanced model can accurately and precisely detect wheat stripe rust summer spores. © 2024 IEEE.

关键词： Adaptive feature fusion Attention mechanism Fungal spore recognition Small target detection YOLOv7

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RGB image and Monochromatic image of hyperspectral image for Identification of apple fungi infection 8

RGB image and Monochromatic image of hyperspectral image for...

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8th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, ISAEECE 2023

作者： Lv, Wenbing Chang, Haoyu Zhang, Shengyu Weng, Shizhuang Zheng, Ling National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University 111 Jiulong Road Hefei China

ISBN: (纸本)9781510666290

Detection of apple fungi infection is significant to provide the customized prevention and control strategies and ensure food safety. In this study, an identification method of infection of Botrytis cinerea and Rhizopus stolonifera was developed using the RGB images and monochromatic images (MIs) of effective wavelengths (EWs) of hyperspectral imaging. RGB images converted by CIE 1931 colour matching functions, and MIs of EWs were screened by random frog from hyperspectral images. U-Net combining data splicing strategy was adopted to segment the region of rot (ROR). Network features of RGB images and MIs of EWs of ROR were extracted by VGG16 and adopted to develop the classification models of fungi infection by using SVM, RF and KNN. The fused features of two-type images obtained the better classification, outperforming the other one-type image, and the optimal accuracy in prediction set of 99.25% was gotten from the SVM model. The proposed method provides the accurate detection of apple fungi infection and is beneficial to improve the quality of apple fruit. © 2023 SPIE. All rights reserved.

关键词： Hyperspectral imaging

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Clustering Network Based on Pre-Clustering center Codebook 18

Clustering Network Based on Pre-Clustering Center Codebook

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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： An, Qiulong Wu, Jianning Huang, Tianqiang Huang, Liqing Luo, Haifeng Ye, Feng College of Computer and Cyber Security Fujian Normal University Digital Fujian Institute of Big Data Security Technology Fujian Provincial Engineering Research Center of Big Data Analysis and Application Fuzhou China

ISBN: (纸本)9798350318401

Most existing deep clustering models attempt to group similar datas by using autoencoders to simultaneously minimize the clustering loss and reconstruction losses. However, these methods do not impose any constraints on the latent space, resulting in intermediate features that are not suitable for clustering. Moreover, they suffer from poor clustering performance when the number of clusters is large. To address this problem, this paper proposes an autoencoder network with preclustered center codebooks for clustering, two codebooks to represent the cluster centers and center offsets, respectively, making the network features more compatible with the clustering task. Finally, We conducted experiments on CIFAR-10 and STL-10 datasets, the results indicate the effectiveness and superiority of the proposed network model. © 2023 IEEE.

关键词： Network coding

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