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Ecological informatics
Elsevier
Ecological informatics

Elsevier

1574-9541

Ecological informatics/Journal Ecological informaticsISTPSCI
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    Flood susceptibility mapping using extremely randomized trees for Assam 2020 floods

    Sachdeva, ShrutiKumar, Bijendra
    12页
    查看更多>>摘要：The year 2020 proved disastrous for the north eastern state of India, Assam. The state witnessed terrible floods in the midst of the pandemic. The current study aims to better understand the role played by various factors that contributed to the deluge. To this end, the current study undertakes a flood susceptibility mapping using a seldom employed decision tree based ensemble machine learning technique of extremely randomized trees (ERT). The model was trained and tested on a flood inventory superimposed with 14 flood influencing factors, namely slope, elevation, aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), slope length, land use, geology, soil type, topographic roughness index (TRI), rainfall, distance from rivers, plan and profile curvature. The model was compared against other mapping techniques and produced an area under the receiver operating characteristic curve (AUC) of 0.901 outperforming others. The generated susceptibility map deduced the presence of low elevation, high rainfall and close proximity to rivers as major factors leading up to the disaster. It prophesizes a very high flood risk for approximately 18.32% of the study area concentrated in the northern and western part of the study region.
      原文链接:
    • NSTL
    • Elsevier

    Hybrid convolution neural network model for a quicker detection of infested maize plants with fall armyworms using UAV-based images

    Ishengoma, Farian S.Rai, Idris A.Ngoga, Said Rutabayiro
    7页
    查看更多>>摘要：Visual detection of plants diseases over a large area is time-consuming, and the results are prone to errors due to the subjective nature of human evaluations. Several automatic disease detection techniques that improve detection time and improve accuracy compared to visual methods exist, yet they are not suitable for immediate detection. In this paper, we propose a hybrid convolution neural network (CNN) model to speed up the detection of fall armyworms (faw) infested maize leaves. Specifically, the proposed system combines unmanned aerial vehicle (UAV) technology, to autonomously capture maize leaves, and a hybrid CNN model, which is based on a parallel structure specifically designed to take advantage of the benefits of both individual models, namely VGG16 and InceptionV3. We compare the performance of the proposed model in terms of accuracy and training time to four existing CNN models, namely VGG16, InceptionV3, XceptionNet, and Resnet50. The results show that compared to existing models, the proposed hybrid model reduces the training time by 16% to 44% compared to other models while exhibiting the most superior accuracy of 96.98%.
      原文链接:
    • NSTL
    • Elsevier

    Camera traps and artificial intelligence for monitoring invasive species and emerging diseases

    Santoro, SimonePerez, IsaacGegundez-Arias, Manuel EmilioCalzada, Javier...
    2页
      原文链接:
    • NSTL
    • Elsevier

    Using RS/GIS for spatiotemporal ecological vulnerability analysis based on DPSIR framework in the Republic of Tatarstan, Russia

    Boori, Mukesh SinghChoudhary, KomalParinger, RustamKupriyanov, Alexander...
    11页
    查看更多>>摘要：The republic of Tatarstan is one of the most growing state in Russia in terms of industrialization and modernization with various natural disasters and intense human activities which brought dramatic changes in the ecological process and then led to serious ecological vulnerability. Therefore this research work proposed an analytical framework based on remote sensing (RS), geographical information system (GIS), and analytical hierarchy process (AHP) for spatiotemporal ecological vulnerability analysis at pixel level from 2010 to 2020 and developed a driver-pressure-state-impact-response (DPSIR) framework based on 23 indicators by the AHP weight method to compute ecological vulnerability index (EVI). Further, EVI was classified into five levels based on natural breaks in ArcGIS software as potential, slight, light, moderate, and heavy levels. All 23 indicators were generated from different remote sensing and socio-economic data, processed through digital image processing techniques in terms of removing errors, projection, standardization, and results were saved in GIS format. Results indicate that from 2010 to 2020, EVI was continuously increased from 0.419 to 0.429, and its changes associated with regional vulnerability events and their impact in the region. The moderate level EVI was covering the highest area in all three years with very few changes and continuously increasing. Results also indicate that higher human-socio-economic activities and pressure on natural resources increased ecological vulnerability. This research work is useful to identify main causes and responsible indicators for ecological vulnerability as well as suitable for real-time EVI mapping, monitoring at any scale and region.
      原文链接:
    • NSTL
    • Elsevier

    High performing ensemble of convolutional neural networks for insect pest image detection

    Maguolo, GianlucaLumini, AlessandraBrahnam, SherylNanni, Loris...
    12页
    查看更多>>摘要：Pest infestation is a major cause of crop damage and lost revenues worldwide. Automatic identification of invasive insects would significantly speed up the recognition of pests and expedite their removal. In this paper, we generated ensembles of CNNs based on different topologies (EfficientNetB0, ResNet50, GoogleNet, ShuffleNet, MobileNetv2, and DenseNet201) optimized with different Adam variants for pest identification. Two new Adam algorithms for deep network optimization based on DGrad are proposed that introduce a scaling factor in the learning rate. Six CNN architectures that vary in their optimization function were trained on the Deng (SMALL), large IP102, and Xie2 (D0) pest data sets. Ensembles were compared and evaluated using several performance indicators. The best performing ensemble, which combined the CNNs using the different Adam variants, including the new ones proposed here, competed with human expert classifications on the Deng data set and achieved state of the art on all three insect data sets: 95.52% on Deng, 74.11% on IP102, and 99.81% on Xie2. Additional tests were performed on data sets for medical imagery classification that further validated the robustness and power of the proposed Adam optimization variants. All MATLAB source code is available at https://github.com/LorisNanni/.
      原文链接:
    • NSTL
    • Elsevier

    A note of appreciation

    Arhonditsis, GeorgeDescoteaux, Danie
    1页
      原文链接:
    • NSTL
    • Elsevier

    Performance analysis of real-time plant species recognition using bilateral network combined with machine learning classifier

    Pearline, S. AnubhaKumar, V. Sathiesh
    13页
    查看更多>>摘要：A real-time plant species recognition under an unconstrained environment is a challenging and time-consuming process. The recognition model should cope up with the computer vision challenges such as scale variations, illumination changes, camera viewpoint or object orientation changes, cluttered backgrounds and structure of leaf (simple or compound). In this paper, a bilateral convolutional neural network (CNN) with machine learning classifiers are investigated in relation to the real-time implementation of plant species recognition. The CNN models considered are MobileNet, Xception and DenseNet-121. In the bilateral CNNs (Homogeneous/Heterogeneous type), the models are connected using the cascade early fusion strategy. The Bilateral CNN is used in the process of feature extraction. Then, the extracted features are classified using different machine learning classifiers such as Linear Discriminant Analysis (LDA), multinomial Logistic Regression (MLR), Naive Bayes (NB), k Nearest Neighbor (k-NN), Classification and Regression Tree (CART), Random Forest Classifier (RF), Bagging Classifier (BC), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). From the experimental investigation, it is observed that the multinomial Logistic Regression classifier performed better compared to other classifiers, irrespective of the bilateral CNN models (Homogeneous MoMoNet, XXNet, DeDeNet; Heterogeneous MoXNet, XDeNet, MoDeNet). It is also observed that the MoDeNet + MLR model attained the stateof-the-art results (Flavia: 98.71%, Folio: 96.38%, Swedish Leaf: 99.41%, custom created Leaf-12: 99.39%), irrespective of the dataset. The number of misprediction/class is highly reduced by utilizing the MoDeNet + MLR model for real-time plant species recognition.
      原文链接:
    • NSTL
    • Elsevier

    Identifying wildlife observations on twitter

    Edwards, ThomasJones, Christopher B.Corcoran, Padraig
    13页
    查看更多>>摘要：Despite the potential of social media for environmental monitoring, concerns remain about the quality and reliability of the information automatically extracted. Notably there are many observations of wildlife on Twitter, but their automated detection is a challenge due to the frequent use of wildlife related words in messages that have no connection with wildlife observation. We investigate whether and what type of supervised machine learning methods can be used to create a fully automated text classification model to identify genuine wildlife observations on Twitter, irrespective of species type or whether Tweets are geo-tagged. We perform experiments with various techniques for building feature vectors that serve as input to the classifiers, and consider how they affect classification performance. We compare three classification approaches and perform an analysis of the types of features that are indicative for genuine wildlife observations on Twitter. In particular, we compare some classical machine learning algorithms, widely used in ecology studies, with state-of-the-art neural network models. Results showed that the neural network-based model Bidirectional Encoder Representations from Transformers (BERT) outperformed the classical methods. Notably this was the case for a relatively small training corpus, consisting of less than 3000 instances. This reflects that fact that the BERT classifier uses a transfer learning approach that benefits from prior learning on a very much larger collection of generic text. BERT performed particularly well even for Tweets that employed specialised language relating to wildlife observations. The analysis of possible indicative features for wildlife Tweets revealed interesting trends in the usage of hashtags that are unrelated to official citizen science campaigns. The findings from this study facilitate more accurate identification of wildlife-related data on social media which can in turn be used for enriching citizen science data collections.
      原文链接:
    • NSTL
    • Elsevier

    Modelling hiding behaviour in a predator-prey system by both integer order and fractional order derivatives

    Barman, DipeshRoy, JyotirmoyAlam, Shariful
    28页
    查看更多>>摘要：Most of the preys are well aware of sensing predation risk. Consequently, to escape from predators they usually adopt several defense mechanisms, specially refuge themselves to become invulnerable. In view of this, a mathematical model has been formulated incorporating prey refuge, where it is assumed that prey refuge is a function of predators availability in the system. It is shown that the model system is well-posed. It has been found that the hiding level and consumption rate of predators have a suitable interrelation between them. Both the parameters act as Hopf bifurcation parameters, but they play opposite role in case of stabilization of the system dynamics. Also, hiding level plays crucial role in maintaining the mean density of both the populations. Furthermore, as hiding behaviour of prey is not instantaneous, so a time delay, namely hiding delay has been introduced to make the model system more realistic and it is observed that the delay parameter destabilizes the system. Modelling approach through fractional calculus has been further deployed to study how the process of forgetting life history influences the dynamical intricacy of the population level dynamics. All the analytical findings have been testified by proper numerical performances.
      原文链接:
    • NSTL
    • Elsevier

    Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning

    Lekunberri, XabierQuincoces, InakiDornaika, FadiArganda-Carreras, Ignacio...
    14页
    查看更多>>摘要：Fishery monitoring programs are essential for effective management of marine resources, as they provide scientists and managers with the necessary data for both the preparation of scientific advice and fisheries control and surveillance. The monitoring is generally done by human observers, both in port and onboard, with a high cost involved. Consequently, some Regional Fisheries Management Organizations (RFMO) are opting for electronic monitoring (EM) as an alternative or complement to human observers in certain fisheries. This is the case of the tropical tuna purse seine fishery operating in the Indian and Atlantic oceans, which started an EM program on a voluntary basis in 2017. However, even when the monitoring is conducted though EM, the image analysis is a tedious task manually performed by experts. In this paper, we propose a cost-effective methodology for the automatic processing of the images already being collected by cameras onboard tropical tuna purse seiners. Firstly, the images are preprocessed to homogenize them across all vessels and facilitate subsequent steps. Secondly, the fish are individually segmented using a deep neural network (Mask R-CNN). Then, all segments are passed through other deep neural network (ResNet50V2) to classify them by species and estimate their size distribution. For the classification of fish, we achieved an accuracy for all species of over 70%, i.e., about 3 out of 4 individuals are correctly classified to their corresponding species. The size distribution estimates are aligned with official port measurements but calculated using a larger number of individuals. Finally, we also propose improvements to the current image capture systems which can facilitate the work of the proposed automation methodology.
      原文链接:
    • NSTL
    • Elsevier