Insect pest image detection and recognition based on bio-inspired methods

TL;DR

This paper introduces a bio-inspired insect pest recognition system combining saliency image preprocessing with convolutional neural networks, achieving state-of-the-art accuracy on pest datasets.

Contribution

It proposes a novel fusion of saliency methods and CNNs for improved insect pest detection and recognition, with extensive evaluation on multiple datasets.

Findings

Achieved 92.43% accuracy on a small dataset.

Achieved 61.93% accuracy on the IP102 dataset.

Ensemble methods outperform individual models.

Abstract

Insect pests recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods and convolutional neural networks. Saliency methods are famous image processing algorithms that highlight the most relevant pixels of an image. In this paper, we use three different saliency methods as image preprocessing and create three different images for every saliency method. Hence, we create 3x3=9 new images for every original image to train different convolutional neural networks. We evaluate the performance of every preprocessing/network couple and we also evaluate the performance of their ensemble. We test our approach on both a small dataset and the large IP102 dataset. Our best ensembles reaches the state of the art accuracy on both the smaller dataset (92.43%) and the IP102 dataset (61.93%), approaching the performance of human experts on the smaller one. Besides, we share our MATLAB code at: https://github.com/LorisNanni/.