https://scholars.lib.ntu.edu.tw/handle/123456789/605940
標題: | Tree-based deep convolutional neural network for hierarchical identification of low-resolution insect images | 作者: | Rustia D.J.A Wu Y.-F Shih P.-Y Chen S.-K Chung J.-Y TA-TE LIN |
關鍵字: | Convolutional neural network;Deep learning;Insect identification;Integrated pest management;Tree-based image classification;Agriculture;Convolution;Convolutional neural networks;Deep neural networks;Image acquisition;Image classification;Pest control;Sensor nodes;Automated identification;Classification methods;Classification performance;Hierarchical identification;Hyper-parameter optimizations;Integrated Pest Management;Probability threshold;Recognition algorithm;Classification (of information) | 公開日期: | 2021 | 卷: | 3 | 起(迄)頁: | 1348-1358 | 來源出版物: | American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021 | 摘要: | The inability to classify insects up to the species level is the current limitation of recently developed automated insect recognition algorithms. Yet, there are situations in integrated pest management (IPM) that call for more precise classification of insects. This research proposes an effective method for automated identification of low-resolution insect images found on sticky paper traps. Insect image samples were collected from sticky paper trap images that were acquired by wireless image sensor nodes installed in an outdoor mango orchard. A tree-based classifier, made up of taxonomically cascaded convolutional neural network (CNN) models, was used to automatically identify the insects on each sticky paper trap from coarse to fine taxonomic levels. The proposed tree-based classification method can classify the insect images up to an F1-score of 0.94, surpassing the performance of an optimized single multi-class image classifier model with an F1-score of 0.87. The proposed method not only boosts the classification performance, but also offers hierarchical predictions that can be used as easily interpretable information. In addition, model hyperparameter optimization, data augmentation, and classification probability threshold tuning can be applied at each level to optimize its performance. This research can be applied to support farmers in selecting IPM components, such as crop variants, pesticides, parasitoids, and more, in order to control the population of different insect species and prevent production loss. ? ASABE 2021 Annual International Meeting |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114204721&doi=10.13031%2faim.202100437&partnerID=40&md5=8859679a9f9c001a534615ed8e9ba365 https://scholars.lib.ntu.edu.tw/handle/123456789/605940 |
DOI: | 10.13031/aim.202100437 |
顯示於: | 生物機電工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。