Deep-Wide Learning Assistance for Insect Pest Classification
Accurate insect pest recognition plays a critical role in agriculture. It is a challenging problem due to the intricate characteristics of insects. In this paper, we present DeWi, novel learning assistance for insect pest classification. With a one-stage and alternating training strategy, DeWi simultaneously improves several Convolutional Neural Networks in two perspectives: discrimination (by optimizing a triplet margin loss in a supervised training manner) and generalization (via data augmentation). From that, DeWi can learn discriminative and in-depth features of insect pests (deep) yet still generalize well to a large number of insect categories (wide). Experimental results show that DeWi achieves the highest performances on two insect pest classification benchmarks (76.44\% accuracy on the IP102 dataset and 99.79\% accuracy on the D0 dataset, respectively). In addition, extensive evaluations and ablation studies are conducted to thoroughly investigate our DeWi and demonstrate its superiority. Our source code is available at https://github.com/toannguyen1904/DeWi.
Code (1)
Tasks
ClassificationData AugmentationTripletSimilar Papers 제목 키워드 기반
InsectMamba: Insect Pest Classification with State Space Model
The classification of insect pests is a critical task in agricultural technology, vital for ensuring food security and environmental sustainability. However, the complexity of pest identification, due to factors like hig…
ClassificationDiversitymodelState Space ModelsAn Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models
Accurate insect pest recognition is significant to protect the crop or take the early treatment on the infected yield, and it helps reduce the loss for the agriculture economy. Design an automatic pest recognition system…
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition
Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the …
ClassificationFine-Grained Image ClassificationGeneral Classificationobject-detection+1High performing ensemble of convolutional neural networks for insect pest image detection
Pest infestation is a major cause of crop damage and lost revenues worldwide. Automatic identification of invasive insects would greatly speedup the identification of pests and expedite their removal. In this paper, we g…
Data AugmentationWeakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification
Citrus juices and fruits are commodities with great economic potential in the international market, but productivity losses caused by mites and other pests are still far from being a good mark. Despite the integrated pes…
ClassificationMultiple Instance LearningWeakly-supervised Learning