Multi-view features fusion for birdsong classification

As important members of the ecosystem, birds are good monitors of the ecological environment. Bird recognition, especially birdsong recognition, has attracted more and more attention in the field of artificial intelligence. At present, traditional machine learning and deep learning are widely used in birdsong recognition. Deep learning can not only classify and recognize the spectrums of birdsong, but also be used as a feature extractor. Machine learning is often used to classify and recognize the extracted birdsong handcrafted feature parameters. As the data samples of the classifier, the feature of birdsong directly determines the performance of the classifier. Multi-view features from different methods of feature extraction can obtain more perfect information of birdsong. Therefore, aiming at enriching the representational capacity of single feature and getting a better way to combine features, this paper proposes a birdsong classification model based multi-view features, which combines the deep features extracted by convolutional neural network (CNN) and handcrafted features. Firstly, four kinds of handcrafted features are extracted. Those are wavelet transform (WT) spectrum, Hilbert-Huang transform (HHT) spectrum, short-time Fourier transform (STFT) spectrum and Mel-frequency cepstral coefficients (MFCC). Then CNN is used to extract the deep features from WT, HHT and STFT spectrum, and the minimal-redundancy-maximal-relevance (mRMR) to select optimal features. Finally, three classification models (random forest, support vector machine and multi-layer perceptron) are built with the deep features and handcrafted features, and the probability of classification results of the two types of features are fused as the new features to recognize birdsong. Taking sixteen species of birds as research objects, the experimental results show that the three classifiers obtain the accuracy of 95.49%, 96.25% and 96.16% respectively for the features of the proposed method, which are better than the seven single features and three fused features involved in the experiment. This proposed method effectively combines the deep features and handcrafted features from the perspectives of signal. The fused features can more comprehensively express the information of the bird audio itself, and have higher classification accuracy and lower dimension, which can effectively improve the performance of bird audio classification.     

基于残差注意力网络模型的浮游植物识别

浮游植物作为水生态系统中最重要的生物组成部分之一,对水环境敏感,在水环境监测中得到了广泛的关注。然而水生环境复杂多样,准确高效地识别浮游植物是监测工作中的一大挑战。当前浮游植物识别方法可分为经典形态学分类、分子标记和人工智能图像识别三类。前两种方法已被广泛采用,但费时费力,不利于监测机构的大规模应用和推广。同样,利用图像进行自动化分类难以在高准确率与高效率上达到平衡。深度学习技术的发展为此提供了新思路。本文提出一种新的深度卷积神经网络RAN-11。该网络以残差注意力网络Attention-56和Attention-92为基础,凭借通道对齐融合主干上的底层特征与顶层特征,通过调整注意力模块和残差快个数以精简结构,并引入了Leaky ReLU激活函数代替ReLU。以太湖11个优势属共计1036张图像为数据来源进行对比验证。除星杆藻外,RAN-11对单一优势属的的查准率都在90%以上,并且有5个优势属达到100%的查准率。RAN-11的识别准确率为95.67%,推理速率为41.5帧/s,不仅比Attention-92（95.19%的准确率,23.6帧/s）更准确,而且比Attention-56（94.71%的准确率,41.2帧/s）更快,真正兼顾了准确率与效率。研究结果表明：（1）RAN-11在查准率、准确率和推理速率上优于原始残差注意力网络,更优于以词包模型为代表的传统图像识别方法;（2）融合多尺度特征、精简网络结构和优化激活函数是提高卷积神经网络性能的有力手段。建立在经典分类基础之上,本文提出新的残差注意力网络来提升浮游植物鉴定技术,并构建出浮游植物自动化识别系统,识别准确率高、易于推广,对于实现水体中浮游植物的自动化监测具有重要意义。     

A brief history of the many faces of detection in ecology and wildlife management

In the early years of the development of ecological methods, detection was considered a relatively simple parameter to estimate. The early closed population estimation techniques of Lincoln and Petersen and the more sophisticated open population models of Leslie, Chitty, Chitty and Jolly, and Seber assumed a relative ease of estimating the detection probability. Wildlife ecologists who knew their animals were always concerned about unequal catchability, and fisheries biologists like Ricker provided models to compensate for unequal catchability in fishery estimation, but it was not until the Colorado mafia published their monograph in 1978 on a series of models that allowed for certain classes of unequal detection that the problem was formalized for closed populations in Program CAPTURE. From the mid-1970s there was a groundswell of publications and a generation of cooperation between mathematicians and ecologists to attack the problem of detection, not only for population estimation but more importantly for disease analysis and pest management. This new synthesis of mathematical and statistical power with ecological insights of the clever ways that animals and plants avoid detection has produced a series of methods that recognised as a critical part of wildlife management in this century.     

基于网络群组特征的生态管理分区——以武汉市为例

生态管理分区是维持区域生态安全、实现城市生态差异化治理的重要手段。然而现有分区方法侧重生态功能属性,较少考虑生态斑块之间联系强度差异,忽视了斑块的群组结构。以武汉市为例构建生态网络,从生态系统结构和功能的视角,结合斑块空间组织和斑块联系强弱,运用凝聚子群方法,提取联系紧密的生态组分,将网络划分为异质性群组,基于网络群组特征和生态景观辐射范围进行分区覆盖分析,并进行分区评价。结果表明：(1)86条生态廊道连接研究区34处生态斑块,进一步形成8个生态群组;(2)多数群组内部连通性较好,北部群组之间联系较强,南部群组之间联系相对较弱;(3)依据群组结构功能特征,形成6大网络群组分区,通过与武汉市经济发展规划分区对比,两者具有较高的一致性;(4)城市外围的分区网络稳定性较好,中部稳定性较差,识别分区内13个重要斑块和16条重要廊道,作为重点发展保护对象;(5)综合分区特征将6大分区确立为生态屏障区、生态控制区、生态改善区、生态修复区、生态开发区以及生态保育区,并提出生态发展差异化保护措施。研究将生态功能属性和空间结构属性进行有机关联并制定分区策略,为区域生态管理分区、生态保护规划提供新视角。     

Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS

Accurate detection of plant leaves is a meaningful and challenging task for developing smart agricultural systems. To improve the performance of detecting plant leaves in natural scenes containing severe occlusion, overlapping, or shape variation, we developed an in situ sweet potato leaf detection method based on a modified Faster R-CNN framework and visual attention mechanism. First, a convolutional block attention module was added to the backbone network to enhance and extract critical features of leaf images by fusing cross-channel information and spatial information. Subsequently, the DIoU-NMS algorithm was adopted to modify the regional proposal network by replacing the original NMS. DIoU-NMS was utilized to reduce missed and incorrect detection in scenes of densely distributed leaves by considering the targets' overlap ratio, distance, and scale. The proposed leaf detection method was tested and evaluated on sweet potato plant images collected in agricultural fields. In the datasets, sweet potato leaves were presented in various sizes and poses, and a large proportion of leaves were occluded or overlapped with each other. The experimental results showed that the proposed leaf detection method outperforms state-of-the-art object detection methods. The mean average precision of the proposed method reached 95.7%, which was 2.9% higher than that of the original Faster R-CNN and 7.0% higher than that of YOLOv5. The proposed method achieved promising performance in detecting dense leaves or occluded leaves and could provide key techniques for applications in smart agriculture and ecological monitoring, such as growth monitoring or plant phenotyping.     

CNN and transformer framework for insect pest classification

Insect pests pose a significant and increasing threat to agricultural production worldwide. However, most existing recognition methods are built upon well-known convolutional neural networks, which limits the possibility of improving pest recognition accuracies. This research attempts to overcome this challenge from a novel perspective, constructing a simplified but very useful network for effective insect pest recognition by combining transformer architecture and convolution blocks. First, the representative features are extracted from the input image using a backbone convolutional neural network. Second, a new transformer attention-based classification head is proposed to sufficiently utilize spatial data from the features. With that, we explore different combinations for each module in our model and abstract our model into a simple and scalable architecture; we introduce more effective training strategies, pretrained models and data augmentation methods. Our models performance was evaluated on the IP102 benchmark dataset and achieved classification accuracies of 74.897% and 75.583% with minimal implementation costs at image resolutions of 224 × 224 pixels and 480 × 480 pixels, respectively. Our model also attains accuracies of 99.472% and 97.935% on the D0 dataset and Li's dataset, respectively, with an image resolution of 224 × 224 pixels. The experimental results demonstrate that our method is superior to the state-of-the-art methods on these datasets. Accordingly, the proposed model can be deployed in practice and provides additional insights into the related research.     

城市森林生态服务价值评估研究进展

随着城市化进程的加快,城市环境问题日益突出,作为城市生态系统的重要子系统之一,城市森林为城市居民提供诸多生态服务.而城市森林生态服务价值评估能够有效指导城市森林建设,为管理部门提供决策依据,从而最大限度地发挥城市森林的生态服务功能.在阐明城市森林及其生态服务价值内涵的基础上,重点对比分析了各类城市森林生态服务价值的评估方法,并根据其发展历程将其归纳为3类:单株树木经济价值评估法;城市森林生态服务综合价值评估法;空间显式景观模型评估法.最后指出现有城市森林生态服务价值评估方法中的不足及今后发展方向,以期丰富城市森林生态服务价值评估理论,并为城市森林的合理规划提供借鉴.     

城市生态基础设施管理研究进展

城市生态基础设施作为城市生态系统的重要组成部分,在维持自然生态过程稳定、促进社会经济发展、保障人居环境质量方面发挥着重要的作用。在快速城市化进程中,对城市生态基础设施进行科学的管理显得尤为重要。在重新明确城市生态基础设施管理概念和内涵的基础上,归纳提炼了4项管理原则,并对现有管理类型进行了梳理。对生态基础设施管理涉及的3个关键问题的常见解决方法进行了总结、分类,并对每类方法的优劣进行了分析讨论。针对此领域的工作做了展望。对于现有城市生态基础设施管理问题与方法的整合研究既有利于明确今后研究重点,也为城市生态基础设施管理提供了科学依据和案例参考。     

织物电极的皮肤-电极接触阻抗测量方法分析

随着可穿戴式健康监测技术的发展,新型心电传感器-织物电极成为人们关注的热点,本文对织物电极的皮肤-电极接触阻抗测量方法进行了综述。首先介绍了织物电极的概念,分析了织物电极的皮肤-电极电化学界面、皮肤-电极电化学界面的等效电路和简化电路模型,得出了皮肤-电极接触阻抗的计算公式;其次,将皮肤-电极接触阻抗的测量方法归纳为直接测量法、参比测量法和模拟皮肤测量法三类,讨论了它们的测量原理和优缺点。本文认为需将模拟皮肤测量法和真实皮肤测量法有机结合,才能有效评价织物电极的阻抗特性,为织物电极的性能评价和心电信号采集电路的设计提供重要依据。最后,本文对织物电极待解决的问题进行了分析讨论。     

我国农业害虫综合防治研究现状与展望

害虫综合防治作为农业生产的一项重要策略,在农业可持续发展中具有举足轻重的作用。近年来,针对我国害虫防治所存在的技术需求,科技部等部门先后通过973计划、863计划、科技支撑计划和农业行业专项等对重要害虫防治研究立项支持。通过这些项目的实施,我国建成了一支由国家和省级科研单位和大学组成的专业科研队伍和研究平台,对害虫监测预警技术、基于生物多样性保护利用的生态调控技术、害虫生物防治技术、化学防治技术、抗虫转基因作物利用技术等方面的研究取得了一系列的重要进展,研究建立了棉花、水稻、玉米、小麦和蔬菜等作物重要害虫的综合防治技术体系,并在农业生产中发挥了重要作用。以基因工程和信息技术为代表的第二次农业技术革命的到来,推动了害虫综合防治的理论发展,为害虫综合防治技术的广泛应用提供了新的机遇。地理信息系统、全球定位系统等信息技术和计算机网络技术的应用,提高了对害虫种群监测和预警的能力和水平,转基因抗虫作物的商业化种植等技术的应用显著增强了对害虫种群的区域性调控效率。针对产业结构调整和全球气候变化所带来的害虫新问题,进一步发展IPM新理论与新技术将成为我国农业昆虫学研究的重要方向之一。     

Modelling dendritic ecological networks in space: an integrated network perspective

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2‐D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2‐D space can be used to explore the influence of multi‐scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).     

Ecological network analysis of urban–industrial ecosystems

Sustainability of urban areas is paramount in the coming years as cities continue to grow in population and resource consumption. A number of methods to model cities have been developed, including material flow analysis and urban metabolism, but these accounting methods do not fully analyze the complex network dynamics present within cities. Ecological network analysis (ENA) provides a new perspective into these urban areas by using metrics designed for analysis of natural ecosystems. This study analyzes 29 urban–industrial ecosystems using ENA, comparing the networks to each other as well as comparing them to previously analyzed eco‐industrial parks and natural food webs. It is found that these systems perform similar to other human‐designed systems, which consistently lack in ecological performance when compared with the natural ecosystems. Additionally, the impact of specific actor types within these networks is shown indicating the importance of industry, agriculture, and the natural environment. Finally, the types of networks are determined to affect the ecological metrics, with the more linear‐based energy networks having the worst performance. This new dataset of ecologically analyzed networks provides a deeper understanding of urban networks and their infrastructure, while providing useful information on how to potentially improve their sustainability.     

茶园害虫生态控制若干问题的探讨

茶园害虫生态控制是一种较新的控制有害生物的策略。本文讨论了茶园害虫生态控制的特点、原理和方法,认为茶园害虫生态控制是在深入了解茶园生态系统内有关因素的特性、动态、以及相关联系的情况下,运用有效的技术和手段,创造不利于害虫而有利于茶叶生产的条件,充分发挥生态系统中各种害虫调控因子的作用,使茶园害虫种群密度在生态系统内长期处在不足以引起经济损失的水平,使整个茶园生态系统高效、低耗和持续发展。同时详细分析了茶园各种生态因子在生态控制中的作用,并提出了今后茶园害虫生态控制的研究方向和工作重点。     

An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification

Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large-scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a convolutional neural network (CNN) and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DenseNet Vision Transformer (DNVT) combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modelling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89 and 74.20%, respectively, which is better than other state-of-the-art methods and has a high practical application value.     

基于图论的两栖类生物栖息地网络规划——以黑斑侧褶蛙为例

生物多样性保护和生物栖息地网络建设是目前我国国土空间规划的重要内容,提升生物栖息地网络的景观功能连接度对生物多样性保护具有重要作用。目前,已有研究对生物栖息地网络规划进行了探索,但在实际规划层面仍缺乏可操作性强的技术方法支撑。本研究采用图论方法,聚焦国土空间规划中生物多样性保护和生态网络建设涉及的进行生物栖息地斑块重要性评价,以确定斑块优先保护次序;寻找最优新增斑块位置,以改善生物栖息地网络景观功能连接度;依据景观功能连接度的降低程度,判断建设项目的影响或评价规划新增建设项目的潜在影响3方面内容,在雄安新区两栖类生物黑斑侧褶蛙(Pelophylax nigromaculata)栖息地网络规划中进行应用研究。结果表明: 图论方法可以有效解决上述3方面的问题;本研究识别的5个最优新增黑斑侧褶蛙栖息地位置使栖息地网络整体景观功能连接度提升19%;通过评价G45高速公路对两栖类生物栖息地网络功能连接度的影响,找出了4个穿越通道设置以减弱G45高速公路的影响。     

Incorporating fast and intelligent control technique into ecology: A Chebyshev neural network-based terminal sliding mode approach for fractional chaotic ecological systems

In the present study, a new neural network-based terminal sliding mode technique is proposed to stabilize and synchronize fractional-order chaotic ecological systems in finite-time. The Chebyshev neural network is implemented to estimate unknown functions of the system. Moreover, through the proposed Chebyshev neural network observer, the effects of external disturbances are fully taken into account. The weights of the Chebyshev neural network observer are adjusted based on adaptive laws. The finite-time convergence of the closed-loop system, which is a new concept for ecological systems, is proven. Then, the dependency of the system on the value of the fractional time derivatives is investigated. Lastly, the proposed control scheme is applied to the fractional-order ecological system. Through numerical simulations, the performance of the developed technique for synchronization and stabilization are assessed and compared with a conventional method. The numerical simulations strongly corroborate the effective performance of the proposed control technique in terms of accuracy, robustness, and convergence time for the unknown nonlinear system in the presence of external disturbances.     

转抗虫基因植物生态安全性研究进展

转抗虫基因植物如Bt棉花等已在美国、中国和澳大利亚等国家大规模商业化种植 ,有关转抗虫基因植物潜在的生态风险已引起广泛的关注。该文综述了转抗虫基因植物研究应用现状与安全性研究进展。主要内容包括 :转抗虫基因植物的种类及其对靶标害虫的抗性 ,对非靶标害虫和天敌发生的影响 ,对农田生态系统生物多样性的影响 ,靶标昆虫的抗性治理及转抗虫基因植物的基因漂移等     

基于网络分析的城市生态空间结构优化——以常州市为例

经济社会的高速发展带来城市的快速扩张,造成城市生态空间的萎缩和生态功能的下降,城市生态安全受到严重威胁。系统研究城市生态空间结构,提出针对性保护和优化措施,对于城市的可持续发展具有重大意义。本研究以常州市为研究区,考虑城市生态空间的自然生态功能和社会服务功能两方面,构建基于自然生态的“源地-廊道”与基于人文生态的“供给-需求”两类生态网络,对于源地廊道生态网络,主要从节点重要性、网络连通性与稳定性进行定量分析,对于供给需求生态网络,主要从节点重要性、供需均衡与稳定性进行定量分析。结果表明: 常州市主城区源地廊道生态网络的连通性与稳定性水平不高,供给需求生态网络的稳定性水平一般且存在服务供给与需求的空间错位。从连通性与稳定性提升角度,提出新增12个源地节点与57条廊道的源地廊道生态网络优化方案;从供需均衡与稳定性提升角度,提出新增22个供给节点的供给需求生态网络优化方案。对比初始源地廊道生态网络,优化网络连通性水平提升10%,稳定性提升0.05;对比初始供给需求生态网络,优化网络的服务水平提升4%,网络稳定性提升0.10。最后,综合两类生态网络,分别针对现状保护斑块与新增节点两类对象,提出了保护与管理实施方案。     

Application of an image and environmental sensor network for automated greenhouse insect pest monitoring

This work presents an automated insect pest counting and environmental condition monitoring system using integrated camera modules and an embedded system as the sensor node in a wireless sensor network. The sensor node can be used to simultaneously acquire images of sticky paper traps and measure temperature, humidity, and light intensity levels in a greenhouse. An image processing algorithm was applied to automatically detect and count insect pests on an insect sticky trap with 93% average temporal detection accuracy compared with manual counting. The integrated monitoring system was implemented with multiple sensor nodes in a greenhouse and experiments were performed to test the system’s performance. Experimental results show that the automatic counting of the monitoring system is comparable with manual counting, and the insect pest count information can be continuously and effectively recorded. Information on insect pest concentrations were further analyzed temporally and spatially with environmental factors. Analyses of experimental data reveal that the normalized hourly increase in the insect pest count appears to be associated with the change in light intensity, temperature, and relative humidity. With the proposed system, laborious manual counting can be circumvented and timely assessment of insect pest and environmental information can be achieved. The system also offers an efficient tool for long-term insect pest behavior observations, as well as for practical applications in integrated pest management (IPM).     

An image enhancement approach for coral reef fish detection in underwater videos

Coral reefs are rich in fisheries and aquatic resources, and the study and monitoring of coral reef ecosystems are of great economic value and practical significance. Due to complex backgrounds and low-quality videos, it is challenging to identify coral reef fish. This study proposed an image enhancement approach for fish detection in complex underwater environments. The method first uses a Siamese network to obtain a saliency map and then multiplies this saliency map by the input image to construct an image enhancement module. Applying this module to the existing mainstream one-stage and two-stage target detection frameworks can significantly improve their detection accuracy. Good detection performance was achieved in a variety of scenarios, such as those with luminosity variations, aquatic plant movements, blurred images, large targets and multiple targets, demonstrating the robustness of the algorithm. The best performance was achieved on the LCF-15 dataset when combining the proposed method with the cascade region-based convolutional neural network (Cascade-RCNN). The average precision at an intersection-over-union (IoU) threshold of 0.5 (AP₅₀) was 0.843, and the F1 score was 0.817, exceeding the best reported results on this dataset. This study provides an automated video analysis tool for marine-related researchers and technical support for downstream applications.