Double Attention-based Lightweight Network for Plant Pest Recognition
This addresses the problem of timely pest recognition for farmers to prevent crop yield losses, though it is incremental as it builds on existing lightweight network approaches.
The paper tackles plant pest recognition from field images by proposing a double attention-based lightweight deep learning architecture, achieving accuracies of 96.61%, 99.08%, and 91.60% on three dataset variants.
Timely recognition of plant pests from field images is significant to avoid potential losses of crop yields. Traditional convolutional neural network-based deep learning models demand high computational capability and require large labelled samples for each pest type for training. On the other hand, the existing lightweight network-based approaches suffer in correctly classifying the pests because of common characteristics and high similarity between multiple plant pests. In this work, a novel double attention-based lightweight deep learning architecture is proposed to automatically recognize different plant pests. The lightweight network facilitates faster and small data training while the double attention module increases performance by focusing on the most pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60% on three variants of two publicly available datasets with 5869, 545 and 500 samples, respectively. Moreover, the comparison results reveal that the proposed approach outperforms existing approaches on both small and large datasets consistently.