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Detection of tea leaf blight from UAV remote sensing images in overcast and low-light conditions

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Hu, Gensheng Zou, Jiahang Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Hefei230601 China

Detecting tea leaf blight (TLB) in UAV remote sensing images is a challenging task. Under overcast and low-light conditions, existing detection methods face problems such as the small size of the TLB spots, their similarity to the backgrounds, and poor clarity, which results in low detection accuracy. To solve these problems, this study proposes a method based on a combination of an image enhancement network and an efficient object detection network. Firstly, the image is feature-enhanced by the detection task-driven data enhancement network, called UAV detection enhancement network (UDENet), to reduce the influence of insufficient light under overcast weather condition and retain the discriminative information conducive to TLB detection. Secondly, TLB is detected in the enhanced UAV images by TLB detection network UTDet. UTDet performs feature extraction on the feature-enhanced UAV remote sensing images through SFMASNet, which effectively improves the feature extraction capability for small objects in UAV remote sensing images. Space-Frequency Multi-Axis Sub-Network (SFMASNet) also avoids the influence of complex backgrounds on the detection results, and improves the detection accuracy through the use of an Aggregated Contextual Attention Sub-Network (ACASNet). Experimental results show that compared with the existing detection methods, the proposed UDENet combined with UTDet gives the best accuracy for the detection of TLB in UAV remote sensing images under overcast and low-light conditions, with values for the precision, recall and mAP of 67.4%, 65.0% and 68.6%, respectively. The proposed detection method achieves a mAP value that is 4.7% higher than the baseline network, and more than 3.6% higher than the state-of-the-art detection methods. © 1963-2012 IEEE.

关键词： Proximity sensors

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Integrated multi-energy load prediction system with multi-scale temporal channel features fusion

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Liu, Dezhi Zhu, Jiaming Wen, Mengyang Liu, Jinpei Chen, Huayou School of Internet Anhui University Anhui Hefei230039 China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui Hefei230601 China Stony Brook Institute at Anhui University Anhui Hefei230039 China School of Big Data and Statistic Anhui University Anhui Hefei230601 China

Accurate multi-energy load prediction is essential for effective energy management and ensuring grid reliability. Existing methods often struggle to fully capture the intricate temporal complexities and inter-variable dependencies present in multivariate time-series data. To address this, we propose a multi-scale temporal channel features fusion-based method for multi-energy load prediction. Firstly, we introduce a Dynamic Channel Weighted Strategy, which adaptively reconstructs intrinsic mode functions derived from Variational Mode Decomposition, reducing the number of channels and computational costs while retaining essential features. Secondly, we develop an Adaptive Channel Fusion LSTM (ACF-LSTM) by integrating learnable graph neural networks and graph convolution operations into traditional LSTM cells, enabling effective modeling of channel correlations in multivariate time series. By modeling multi-energy load data from dual perspectives using the ACF-LSTM, we construct a Temporal-Channel Feature Fusion (TCFF) block that combines both time and frequency domains for comprehensive feature extraction. A learnable frequency selection mechanism within the TCFF block adaptively identifies relevant frequency components, enhancing the model's ability to capture periodic patterns and long-term dependencies. Experiments on the Power Consumption dataset show that our model achieves a significant improvement in accuracy, reducing MAE by 38.60% and RMSE by 32.56%, highlighting its reliability and generalization for multi-energy load prediction. © 2025 Elsevier Ltd

关键词： Integrated energy system Multi-energy load prediction Residual decomposition Sparse graph learning Temporal channel features fusion

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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On time slices, time complexity, and energy increase in implementing quantum adiabatic evolution by circuit model

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The European Physical Journal D 2025年第5期79卷 1-6页

作者： Sun, Jie Qian, Ling Cai, Dunbo Huang, Zhiguo Lu, Songfeng School of Internet Anhui University Hefei China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei China Fin China-Anhui University Joint Laboratory for Financial Big Data Research Hefei Financial China Information & Technology Co. Ltd. Hefei China China Mobile (Suzhou) Software Technology Company Limited Suzhou China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

In this paper, motivated by previous related works on a kind of quantum local adiabatic evolution, we mainly study the circuit model of its corresponding quantum global adiabatic search algorithm, in which the evolving paths functions in the system Hamiltonian have a general form instead of the usual linear interpolating ones which are widely investigated in the literature. The main conclusion found here can be summarized as follows. Though this quantum adiabatic computation can demonstrate various algorithmic performances, when considering implementing it by a quantum circuit, the time slices thus produced always keep as an invariant which is proportional to inverse square of the overlap between the initial and final states in the problem of interest and also equal to that of multiplying the time complexity and the energy together, by noting that compared with the usual adiabatic evolutions, there is being a great increase of energy of the system. As far as we know, the result exposed here is new and is hoped to be useful for further understanding the kind of quantum adiabatic evolution itself and its connection with the quantum circuit model. Also, we hope the main conclusion here will be ubiquitous when considering applying this kind of quantum adiabatic evolution to solve the problems beyond quantum search.

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An Anchor-Free Refining Feature Pyramid Network for Dense and Multioriented Wheat Spikes Detection under UAV

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jiao, Lin Liu, Haiyun Liang, Zheng Chen, Peng Wang, Rujing Liu, Kang Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Internet Hefei230601 China Hefei Institutes of Physical Science Chinese Academy of Sciences Institute of Intelligent Machines Hefei230031 China University of Science and Technology of China Science Island Branch Graduate School Anhui Hefei230031 China Anhui University School of Internet Hefei230031 China School of Internet Institutes of Physical Science and Information Technology Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui Hefei230601 China Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Science Hefei230031 China Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong

The number of wheat spikes is a crucial index for evaluating the yield, and the precise detection of wheat spikes in an image plays an important role. Among various methods, deep learning-based approaches show impressive results in the task of wheat spike detection. However, the precise detection and recognition of wheat spike encounters large challenges due to complicated backgrounds, arbitrary orientations, and dense distribution in wheat spike images. To alleviate these issues, we have developed an anchor-free refining feature pyramid network (AFRFPN) that gets rid of horizontal bounding boxes (HBBs) from the network. First, the refining feature pyramid network (RFPN) has been introduced into extract richer features of wheat spike with highly variant appearances and multiple scales. Then, learning from the idea of coarse-to-fine, the two-stage anchor-free oriented detection (AFOD) module has been designed. The AFOD module first generates a set of coarse detection (CoDet) results in the way of anchor-free and then further fines them to achieve high-quality predicting bounding boxes (BBs). The number of wheat spike images is insufficient, resulting in poor performance of wheat spike detection modules. To mitigate the lack of the data in the task of oriented wheat spike detection, based on the global wheat head detection (GWHD) dataset, we released a new large-scale wheat spike dataset by relabeling the samples, termed it as rotated GWHD (R-GWHD) dataset. Massive experiments show that the proposed method can achieve 90.6% mAP and 96.7% recall, outperforming other state-of-the-art methods. Additionally, the experiments related to the counting of wheat spikes have been conducted, showing that the developed module can achieve the MAE of 4.95 and RMSE of 7.68, which demonstrates the excellent performance of the proposed method. © 2024 IEEE.

关键词： Deep learning

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

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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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Invisible damage classification of crispy pears based on the combination of hyperspectral and thermal imaging using COGhost_ShuffleNet 5

Invisible damage classification of crispy pears based on the...

引用

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

作者： Zheng, Ling Wu, Feihong Yang, Huiwen Chen, Qun Anhui University National Engineering Research Venter for Agro-Ecological Big Data Analysis & Application Hefei China

ISBN: (纸本)9798350373820

Identifying and classifying invisible damage in crispy pears is vital for ensuring high-quality grading. Despite the demonstrated potential of hyperspectral imaging (HSI) and infrared thermal imaging (IR) for fruit identification, the performance of damage recognition is constrained by the low resolution of HSI images and the spectral redundancy. Additionally, IR exhibits suboptimal performance in identifying multiple types of damage. In this study, we developed a crispy pear damage identification method using a combination of multiple data inputs and an improved ShuffleNetV2. Firstly, this paper registers HSI and IR using the radiation-insensitive feature transform (RIFT) algorithm, obtaining the damaged areas of crisp pears through registration. Secondly, it extracts the HSI spectra from the damaged areas and visualizes the spectra through Gramian Angular Fields (GAF) two-dimensional imaging. Next, it investigates the optimal combination obtained under the fusion of multiple data, aiming to achieve the highest accuracy under the most compatible ShuffleNetV2 model. The optimal combination identified is IR-Band1-Band2-Band3-GAF. Finally, facing the multiple data inputs, reducing network parameters and Flops is crucial for the rapid and efficient damage identification of crisp pears. Therefore, we propose optimizations for *** Ghost module was used to replace the 1 × 1 convolution in the two basic unit modules of ShuffleNetV2, and Ghost_ShuffleNet used convolution optimization (CO) to construct the COGhost_ShuffleNet classification model. We used the optimal data combination as input for identification and classification on this model. COGhost_ShuffleNet achieved the best recognition performance with an ACCP of 98.93%, 1.158M parameters, and 0.086G FLOPS, outperforming other classification methods and data input combinations. Overall, this method accurately and reliably identifies crispy pear damage and can be extended to analyze other quality indicators

关键词： Hyperspectral imaging

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Finite-time complex projective synchronization of fractional-order complex-valued uncertain multi-link network and its image encryption application

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Chinese Physics B 2022年第11期31卷 244-255页

作者： Yong-Bing Hu Xiao-Min Yang Da-Wei Ding Zong-Li Yang School of Electronics and Information Engineering Anhui UniversityHefei 230601China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China

Multi-link networks are universal in the real world such as relationship networks,transportation networks,and communication *** is significant to investigate the synchronization of the network with *** this paper,considering the complex network with uncertain parameters,new adaptive controller and update laws are proposed to ensure that complex-valued multilink network realizes finite-time complex projective synchronization(FTCPS).In addition,based on fractional-order Lyapunov functional method and finite-time stability theory,the criteria of FTCPS are derived and synchronization time is given which is associated with fractional order and control ***,numerical example is given to verify the validity of proposed finite-time complex projection strategy and analyze the relationship between synchronization time and fractional order and control ***,the network is applied to image encryption,and the security analysis is carried out to verify the correctness of this method.

关键词： multi-links network fractional order complex-valued network finite-time complex projective synchronization image encryption

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Rapid distinguish of edible oil adulteration using a hyperspectral spectroradiometer

Rapid distinguish of edible oil adulteration using a hypersp...

引用

2019 International Conference on Electronic engineering and Informatics, EEI 2019

作者： Weng, Shizhuang Pan, Fangfang Yu, Shuan Guo, Bingqing National Engineering Research Center for Agro-Ecological Big Data Analysis &application Anhui University Hefei China

ISBN: (纸本)9781728140766

The adulteration of edible oil is one of the widely existing oil quality problems. In this study, a hyperspectral spectroradiometer was utilised to discriminate sesame and rapeseed oil adulteration rapidly using soybean oil. The reflectance spectra of 350-2500 nm were obtained from different adulterated oil samples using the hyperspectral spectroradiometer. Support Vector Machine (SVM) and random forest (RF) were used for spectral analysis to classify adulteration types. And RF achieved an accuracy of 100%. Considering the redundant information of reflectance spectra, the optimal wavelengths were selected by Principal Component analysis to increase processing speed and simplify the model for subsequent applications. With the spectra of eight selected wavelengths, the accuracy of SVM achieved 92.86%. These results indicated that a hyperspectral spectroradiometer can distinguish edible oil adulteration. © 2019 IEEE.

关键词： Support vector machines

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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 ...

引用

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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