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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Deep Feature-Based Classifiers for Fruit Fly Identification (Diptera: Tephritidae)
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Deep Feature-Based Classifiers for Fruit Fly Identification (Diptera: Tephritidae)

机译：基于深度特征的果蝇识别器（双翅目：天蛾科）

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摘要

Fruit flies has a big biological and economic importance for the farming of different tropical and subtropical countries in the World. Specifically in Brazil, third largest fruit producer in the world, the direct and indirect losses caused by fruit flies can exceed USD 120 million/year. These losses are related to production, the cost of pest control and export markets. One of the most economically important fruit flies in the America belong to the genus Anastrepha, which has approximately 300 known species, of which 120 are recorded in Brazil. However, less than 10 species are economically important and are considered pests of quarantine significance by regulatory agencies. The extreme similarity among the species of the genus Anastrepha makes its manual taxonomic classification a nontrivial task, causing onerous and very subjective results. In this work, we propose an approach based on deep learning to assist the scarce specialists, reducing the time of analysis, subjectivity of the classifications and consequently, the economic losses related to these agricultural pests. In our experiments, five deep features and nine machine learning techniques have been studied for the target task. Furthermore, the proposed approach have achieved similar effectiveness results to state-of-art approaches.
机译：果蝇对世界上不同的热带和亚热带国家的耕种具有重要的生物学和经济意义。特别是在世界第三大水果生产国巴西，果蝇造成的直接和间接损失每年可能超过1.2亿美元。这些损失与生产，虫害控制成本和出口市场有关。在美国，经济上最重要的果蝇之一是Anastrepha属，它有大约300种已知物种，其中120种在巴西有记录。但是，只有不到10个物种在经济上很重要，并且被监管机构认为具有检疫意义的有害生物。 Anastrepha属物种之间的极端相似性使其手动分类学分类成为一项艰巨的任务，从而导致繁重且非常主观的结果。在这项工作中，我们提出了一种基于深度学习的方法，以协助稀缺的专家，减少分析时间，分类的主观性，从而减少与这些农业害虫相关的经济损失。在我们的实验中，针对目标任务研究了五种深层功能和九种机器学习技术。此外，所提出的方法已经获得了与现有技术方法相似的有效性结果。

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