论文标题
MONARCHNET：将君主蝴蝶与具有相似表型的蝴蝶物种区分开
MonarchNet: Differentiating Monarch Butterflies from Butterflies Species with Similar Phenotypes
论文作者
论文摘要
近年来，君主蝴蝶的标志性迁移模式受到许多因素的威胁，从气候变化到农药使用。为了跟踪人口的趋势，科学家以及公民科学家必须准确地识别个人。这对于研究君主蝴蝶的研究是独特的关键，因为还有其他种类的蝴蝶，例如老兄蝴蝶，它们是“外观相似的”（由濒危野生动物和动植物的国际贸易公约创造的），具有相似的表型。为了解决这个问题并有助于更有效的识别，我们提出了君主，这是第一个全面的数据集，该数据集由用于君主和五种相似物种的蝴蝶图像组成。我们训练基线深度学习分类模型，以作为区分君主蝴蝶及其各种外观的工具。我们试图通过为这些特定蝴蝶物种的计算分类提供新的方法来研究生物多样性和蝴蝶生态学的研究。最终目标是帮助科学家以最精确，最有效的方式跟踪君主蝴蝶的种群和迁移趋势。
In recent years, the monarch butterfly's iconic migration patterns have come under threat from a number of factors, from climate change to pesticide use. To track trends in their populations, scientists as well as citizen scientists must identify individuals accurately. This is uniquely key for the study of monarch butterflies because there exist other species of butterfly, such as viceroy butterflies, that are "look-alikes" (coined by the Convention on International Trade in Endangered Species of Wild Fauna and Flora), having similar phenotypes. To tackle this problem and to aid in more efficient identification, we present MonarchNet, the first comprehensive dataset consisting of butterfly imagery for monarchs and five look-alike species. We train a baseline deep-learning classification model to serve as a tool for differentiating monarch butterflies and its various look-alikes. We seek to contribute to the study of biodiversity and butterfly ecology by providing a novel method for computational classification of these particular butterfly species. The ultimate aim is to help scientists track monarch butterfly population and migration trends in the most precise and efficient manner possible.
