The conventional butterfly identification method is based on their different morphological characters namely wing-venation, color, shape, patterns and through the dissection studies and molecular techniques which are tedious, expensive and highly time-consuming. To overcome the above aforesaid challenges, a new butterfly identification system using butterfly images has been designed to instantly identify the butterfly with high accuracy. In this study, we construct a new butterfly dataset with 34,024 butterfly images belonging to 315 species from India. We propose and prove the effectiveness of new data augmentation techniques on our dataset. To identify butterflies using photographic images, we built eleven new Deep Convolutional Neural Network (DCNN) butterfly classifier models using eleven pre-trained architectures namely ResNet-18, ResNet-34, ResNet-50, ResNet-121, ResNet-152, Alex-Net, DenseNet-121, DenseNet-161, VGG-16, VGG-19 and SqueezeNet-v1.1. The different model's classification results were compared and the proposed technique achieved a maximum top-1 accuracy(94.44%), top-3 accuracy(98.46%) and top-5 accuracy(99.09%) using ResNet-152 model, followed by DenseNet-161 model achieved the top-1 accuracy(94.31%), top-3 accuracy (98.07%) and top-5 accuracy (98.66%). The results suggest that models can be assertively used to identify butterflies in India.

常规的蝴蝶识别方法是基于它们的不同形态特征，即机翼通风，颜色，形状，样式，以及繁琐，昂贵和费时的解剖研究和分子技术。为了克服上述挑战，已经设计了一种新的使用蝴蝶图像的蝴蝶识别系统，可以以高准确度立即识别蝴蝶。在这项研究中，我们使用来自印度的315个物种的34,024个蝴蝶图像构建了一个新的蝴蝶数据集。我们提出并证明了新的数据增强技术在我们的数据集上的有效性。为了使用摄影图像识别蝴蝶，我们使用11种经过预先训练的架构，即ResNet-18，ResNet-34，ResNet-50，ResNet-121，ResNet-152，Alex-Net，DenseNet-121，DenseNet-161，VGG-16，VGG-19和SqueezeNet-v1.1。比较了不同模型的分类结果，使用ResNet-152模型，所提出的技术使用topNet 152模型，达到了最高top-1准确性（94.44％），top-3准确性（98.46％）和top-5准确性（99.09％）。 161型达到了前1个精度（94.31％），前3个精度（98.07％）和前5个精度（98.66％）。结果表明，可以肯定地使用模型来识别印度的蝴蝶。紧随其后的是DenseNet-161模型，获得了前1位的准确性（94.31％），前3位的准确性（98.07％）和前5位的准确性（98.66％）。结果表明，可以肯定地使用模型来识别印度的蝴蝶。紧随其后的是DenseNet-161模型，其准确性达到了前1名（94.31％），前3名（98.07％）和前5名（98.66％）。结果表明，可以肯定地使用模型来识别印度的蝴蝶。