Nansen Christian, Ribeiro Leandro Prado, Dadour Ian, Roberts John Dale
Department of Entomology and Nematology, University of California Davis, Davis, California, United States of America.
Department of Entomology and Acarology, University of São Paulo, Piracicaba, São Paulo, Brazil.
PLoS One. 2015 Apr 29;10(4):e0124866. doi: 10.1371/journal.pone.0124866. eCollection 2015.
Computer vision and reflectance-based analyses are becoming increasingly important methods to quantify and characterize phenotypic responses by whole organisms to environmental factors. Here, we present the first study of how a non-destructive and completely non-invasive method, body reflectance profiling, can be used to detect and time stress responses in adult beetles. Based on high-resolution hyperspectral imaging, we acquired time series of average reflectance profiles (70 spectral bands from 434-876 nm) from adults in two beetle species, maize weevils (Sitophilus zeamais) and larger black flour beetles (Cynaus angustus). For each species, we acquired reflectance data from untreated controls and from individuals exposed continuously to killing agents (an insecticidal plant extract applied to maize kernels or entomopathogenic nematodes applied to soil applied at levels leading to ≈100% mortality). In maize weevils (exposed to hexanic plant extract), there was no significant effect of the on reflectance profiles acquired from adult beetles after 0 and 12 hours of exposure, but a significant treatment response in spectral bands from 434 to 550 nm was detected after 36 to 144 hours of exposure. In larger black flour beetles, there was no significant effect of exposure to entomopathogenic nematodes after 0 to 26 hours of exposure, but a significant response in spectral bands from 434-480 nm was detected after 45 and 69 hours of exposure. Spectral bands were used to develop reflectance-based classification models for each species, and independent validation of classification algorithms showed sensitivity (ability to positively detect terminal stress in beetles) and specificity (ability to positively detect healthy beetles) of about 90%. Significant changes in body reflectance occurred at exposure times, which coincided with published exposure times and known physiological responses to each killing agent. The results from this study underscore the potential of hyperspectral imaging as an approach to non-destructively and non-invasively quantify stress detection in insects and other animals.
计算机视觉和基于反射率的分析正日益成为量化和表征整个生物体对环境因素的表型反应的重要方法。在此，我们首次研究了一种非破坏性且完全非侵入性的方法——身体反射率剖析，如何用于检测成年甲虫的应激反应并确定其时间。基于高分辨率高光谱成像，我们获取了两种甲虫（玉米象（Sitophilus zeamais）和大谷盗（Cynaus angustus））成虫的平均反射率剖面的时间序列（434 - 876纳米的70个光谱带）。对于每个物种，我们从未经处理的对照以及持续暴露于致死剂（应用于玉米粒的杀虫植物提取物或应用于土壤的昆虫病原线虫，剂量导致约100%死亡率）的个体中获取反射率数据。在玉米象（暴露于己烷植物提取物）中，暴露0小时和12小时后，对成年甲虫获取的反射率剖面没有显著影响，但在暴露36至144小时后，在434至550纳米的光谱带中检测到显著的处理反应。在大谷盗中，暴露0至26小时后，暴露于昆虫病原线虫没有显著影响，但在暴露45小时和69小时后，在434 - 480纳米的光谱带中检测到显著反应。光谱带被用于为每个物种开发基于反射率的分类模型，分类算法的独立验证显示敏感性（在甲虫中阳性检测末期应激的能力）和特异性（阳性检测健康甲虫的能力）约为90%。身体反射率的显著变化发生在暴露时间，这与已发表的暴露时间以及对每种致死剂已知的生理反应一致。这项研究的结果强调了高光谱成像作为一种非破坏性和非侵入性地量化昆虫和其他动物应激检测方法的潜力。
