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首页> 外文期刊>Turkish Journal of Agriculture & Forestry >Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species
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Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species

机译：在线半自动化害虫可识别性中的人工神经网络：具有2个蓟马物种的应用案例

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摘要

Being faced with practical problems in pest identification, we present a methodical paper based on artificial neural networks to discriminate morphologically very similar species, H trips sambuci Heeger, 1854 and Thr'tpsfuscipmms Haliday, 1836 (Thysanoptera: Thripinae), as an applied case for more general use. The artificially intelligent system may be successfully applied as a credible, online, semiautomated identification tool that extracts hidden information from noisy data, even when the standardcharacters have much overlap and the common morphological keys hint at the practical problem of high morphological plasticity. Statistical analysis of 17 characters, measured or determined for each Thrips fuscipennis and T. sambuci specimen (reared fromlarvae in our laboratories), including 15 quantitative morphometric variables, was performed to elucidate morphological plasticity, detect eventual outliers, and visualize differences between the studied taxa. The computational strategy applied in thisstudy includes a set of statistical tools (factor analysis, correlation analysis, principal component analysis, and linear discriminant analysis) followed by the application of a multilayer perceptron artificial neural network system, which models functions of almost arbitrary complexity. This complex approach has proven the existence of 2 separate species: T. fuscipennis and T. sambuci, All the specimens could be clearly distinguished with 2 distinct subgroups for each species, determined by sex. In conclusion, the use of an optimal 3-layer ANN architecture (17, 4,1) enables fast and reliable 100% classification as proven during the extensive verification process.
机译：面对有害生物识别方面的实际问题，我们提出了一种基于人工神经网络的有条理的论文，以区分形态上非常相似的物种，如H Trips sambuci Heeger，1854年和Thr'tpsfuscipmms Haliday，1836年（Thysanoptera：Thripinae），更通用。人工智能系统可以成功地用作一种可靠的，在线的，半自动的识别工具，该工具可以从嘈杂的数据中提取隐藏的信息，即使标准字符重叠很多且通用的形态学键暗示了高形态可塑性的实际问题也是如此。对17种特征进行统计分析，以测定或鉴定出每条蓟马和桑巴线虫标本（在我们实验室中是从幼虫中采集的），包括15个定量形态变量，以阐明形态可塑性，检测最终的异常值并可视化所研究分类群之间的差异。本研究中采用的计算策略包括一组统计工具（因子分析，相关性分析，主成分分析和线性判别分析），然后应用多层感知器人工神经网络系统，该系统对几乎任意复杂的函数进行建模。这种复杂的方法已证明存在2个独立的物种：T。fuscipennis和T. sambuci。所有标本都可以通过性别明确区分，每个物种有2个不同的亚组。总之，最佳的3层ANN架构（17，4,1）的使用可实现快速可靠的100％分类，这在广泛的验证过程中已得到证明。

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