  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.
