Research on insect pest image detection and recognition based on bio-inspired methods

Insect pest 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 creat 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 of both a small dataset and on the large IP102 dataset. Our ensemble reaches the state of the art accuracy in both datasets, approaching the performance of human experts on the smaller one. Besides, we share our MATLAB code at: https://github.com/LorisNanni/.