  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 is necessary
because manual recognition is slow, time-consuming, and expensive. The
Image-based pest classifier using the traditional computer vision method is not
efficient due to the complexity. Insect pest classification is a difficult task
because of various kinds, scales, shapes, complex backgrounds in the field, and
high appearance similarity among insect species. With the rapid development of
deep learning technology, the CNN-based method is the best way to develop a
fast and accurate insect pest classifier. We present different convolutional
neural network-based models in this work, including attention, feature pyramid,
and fine-grained models. We evaluate our methods on two public datasets: the
large-scale insect pest dataset, the IP102 benchmark dataset, and a smaller
dataset, namely D0 in terms of the macro-average precision (MPre), the
macro-average recall (MRec), the macro-average F1- score (MF1), the accuracy
(Acc), and the geometric mean (GM). The experimental results show that
combining these convolutional neural network-based models can better perform
than the state-of-the-art methods on these two datasets. For instance, the
highest accuracy we obtained on IP102 and D0 is $74.13\%$ and $99.78\%$,
respectively, bypassing the corresponding state-of-the-art accuracy: $67.1\%$
(IP102) and $98.8\%$ (D0). We also publish our codes for contributing to the
current research related to the insect pest classification problem.
