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 to train 4 different convolutional neural networks for every saliency method. We obtain several trained networks. We evaluate the performance of every preprocessing/network couple and we also evaluate the performance of their ensemble. The best stand-alone network is ShuffleNet with no preprocessing. However, our ensemble reaches the state of the art accuracy in a publicly available insect pest dataset, approaching the performance of human experts. Besides, we share our MATLAB code at: https://github.com/LorisNanni/.

翻译：昆虫的昆虫识别对于世界许多地方的作物保护是必要的。 在本文中, 我们建议基于突出方法与进化神经网络的融合, 自动分类。 盐度方法是著名的图像处理算法, 突出一个图像的最相关像素。 在本文中, 我们使用三种不同的突出方法作为图像预处理方法, 来为每个突出方法培训4个不同的进化神经网络。 我们获得了几个经过培训的网络。 我们评估了每一个预处理/网络夫妇的性能, 我们还评估了他们组合的性能。 最好的独立网络是没有预处理的ShuffleNet 。 然而, 我们的集合在公开提供的昆虫虫害数据集中达到了艺术准确性, 接近了人类专家的性能。 此外, 我们分享了我们的MATLAB代码 https://github.com/LorisNanni/ 。