LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 5 of total 5

Search options

  1. Article: Automated garden-insect recognition using improved lightweight convolution network

    Yang, Zhankui / Yang, Xinting / Li, Ming / Li, Wenyong

    Information processing in agriculture. 2021 Dec. 26,

    2021  

    Abstract: Automated recognition of insect category, which currently is performed mainly by agriculture experts, is a challenging problem that has received increasing attention in recent years. The goal of the present research is to develop an intelligent mobile- ... ...

    Abstract Automated recognition of insect category, which currently is performed mainly by agriculture experts, is a challenging problem that has received increasing attention in recent years. The goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile terminals. State-of-the-art lightweight convolutional neural networks (such as SqueezeNet and ShuffleNet) have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters, thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited memory. In this research, we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational cost. In addition, we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the time-delay problems in the field. Experiments demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64% with less training time relative to other classical convolutional neural networks. We have also verified the results that the improved SqueezeNet model has a 2.3% higher than of the original model in the open insect data IP102.
    Keywords agriculture ; automation ; computer software ; gardens ; information ; insects ; memory
    Language English
    Dates of publication 2021-1226
    Publishing place Elsevier B.V.
    Document type Article
    Note Pre-press version
    ZDB-ID 2732690-1
    ISSN 2214-3173
    ISSN 2214-3173
    DOI 10.1016/j.inpa.2021.12.006
    Database NAL-Catalogue (AGRICOLA)

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article: Small-sample learning with salient-region detection and center neighbor loss for insect recognition in real-world complex scenarios

    Yang, Zhankui / Yang, Xinting / Li, Ming / Li, Wenyong

    Computers and electronics in agriculture. 2021 June, v. 185

    2021  

    Abstract: Most real-world scenarios face the problems of small-sample learning and fine-grained recognition. For many rare insect classes, collecting a large number of training samples is infeasible or even impossible. In contrast, humans are able to recognize a ... ...

    Abstract Most real-world scenarios face the problems of small-sample learning and fine-grained recognition. For many rare insect classes, collecting a large number of training samples is infeasible or even impossible. In contrast, humans are able to recognize a new object class with little supervision. This motivates us to address the problems of small-sample recognition and fine-grained recognition for insects by combining recognition and localization; this can provide an effective remedy for data scarcity and the two techniques can bootstrap from each other. In this paper, we propose a saliency-detection model to localize the key regions that have the largest discriminative features for fine-grained insect classification. The learner learns to predict foreground and background masks for such localization, having been trained on a training set annotated with bounding boxes. Additionally, to further generate discriminative features, a center neighbor loss function is used to construct a robust feature-space distribution. The proposed model is trained end-to-end on our small-sample learning dataset, which comprises 220 insect categories from a real-world complex environment. Compared with the method using prototypical networks, the proposed method achieves a superior performance, with a mean recognition rate (top-5 accuracy) of 57.65%, and can effectively recognize insects under small-sample and complex-scene conditions.
    Keywords agriculture ; data collection ; electronics ; insects ; models
    Language English
    Dates of publication 2021-06
    Publishing place Elsevier B.V.
    Document type Article
    Note NAL-AP-2-clean
    ZDB-ID 395514-x
    ISSN 0168-1699
    ISSN 0168-1699
    DOI 10.1016/j.compag.2021.106122
    Database NAL-Catalogue (AGRICOLA)

    Full text online

    More links

    Kategorien

    In stock of ZB MED Bonn / Germany

    Z 4541: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article: Detection of Small-Sized Insects in Sticky Trapping Images Using Spectral Residual Model and Machine Learning.

    Li, Wenyong / Yang, Zhankui / Lv, Jiawei / Zheng, Tengfei / Li, Ming / Sun, Chuanheng

    Frontiers in plant science

    2022  Volume 13, Page(s) 915543

    Abstract: One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly ( ...

    Abstract One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (
    Language English
    Publishing date 2022-06-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2613694-6
    ISSN 1664-462X
    ISSN 1664-462X
    DOI 10.3389/fpls.2022.915543
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article: Classification and detection of insects from field images using deep learning for smart pest management: A systematic review

    Li, Wenyong / Zheng, Tengfei / Yang, Zhankui / Li, Ming / Sun, Chuanheng / Yang, Xinting

    Ecological informatics. 2021 Dec., v. 66

    2021  

    Abstract: Insect pest is one of the main causes affecting agricultural crop yield and quality all over the world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction and control actions. The great breakthrough of deep learning ( ... ...

    Abstract Insect pest is one of the main causes affecting agricultural crop yield and quality all over the world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction and control actions. The great breakthrough of deep learning (DL) technology has resulted in its successful applications in various fields, including automatic insect pest monitoring. DL creates both new strengths and a series of challenges for data processing in smart pest monitoring (SPM). This review outlines the technical methods of DL frameworks and their applications in SPM with emphasis on insect pest classification and detection using field images. The methodologies and technical details evolved in insect pest classification and detection using DL are summarized and distilled during different processing stages: image acquisition, data preprocessing and modeling techniques. Finally, a general framework is provided to facilitate the smart insect monitoring and future challenges and trends are highlighted. In a word, our purpose is to provide researchers and technicians with a better understanding of DL techniques and their state-of-art achievements in SPM, which can promote the implement of various SPM applications.
    Keywords crop yield ; crops ; insect pests ; prediction ; systematic review
    Language English
    Dates of publication 2021-12
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 2212016-6
    ISSN 1878-0512 ; 1574-9541
    ISSN (online) 1878-0512
    ISSN 1574-9541
    DOI 10.1016/j.ecoinf.2021.101460
    Database NAL-Catalogue (AGRICOLA)

    Full text online

    More links

    Kategorien

    In stock of ZB MED Bonn / Germany

    Z 7643: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article: Detecting Pests From Light-Trapping Images Based on Improved YOLOv3 Model and Instance Augmentation.

    Lv, Jiawei / Li, Wenyong / Fan, Mingyuan / Zheng, Tengfei / Yang, Zhankui / Chen, Yaocong / He, Guohuang / Yang, Xinting / Liu, Shuangyin / Sun, Chuanheng

    Frontiers in plant science

    2022  Volume 13, Page(s) 939498

    Abstract: Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting ... ...

    Abstract Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments. First, a dataset containing nine common maize pests is constructed after an image augmentation based on image cropping. Then, a linear transformation method is proposed to optimize the anchors generated by the k-means clustering algorithm, which can improve the matching accuracy between anchors and ground truths. In addition, two residual units are added to the second residual block of the original YOLOv3 network to obtain more information about the location of the underlying small targets, and one ResNet unit is used in the feature pyramid network structure to replace two DBL(Conv+BN+LeakyReLU) structures to enhance the reuse of pest features. Experiment results show that the mAP and mRecall of our proposed method are improved by 6.3% and 4.61%, respectively, compared with the original YOLOv3. The proposed method outperforms other state-of-the-art methods (SSD, Faster-rcnn, and YOLOv4), indicating that the proposed method achieves the best detection performance, which can provide an effective model for the realization of intelligent monitoring of maize pests.
    Language English
    Publishing date 2022-07-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2613694-6
    ISSN 1664-462X
    ISSN 1664-462X
    DOI 10.3389/fpls.2022.939498
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top