Tree-based deep convolutional neural network for hierarchical identification of low-resolution insect images
Journal
American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021
Journal Volume
3
Pages
1348-1358
Date Issued
2021
Author(s)
Abstract
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
Subjects
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)
SDGs
Type
conference paper