Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, MG 36570-900, Brazil.
Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, MG 36570-900, Brazil.
J Hazard Mater. 2024 Dec 5;480:136425. doi: 10.1016/j.jhazmat.2024.136425. Epub 2024 Nov 6.
Pesticide poisoning constantly threatens bees as they forage for resources in pesticide-treated crops. This poisoning requires thorough investigation to identify its causes, underscoring the importance of reliable pesticide detection methods for bee monitoring. Infrared spectroscopy provides reflectance data across hundreds of spectral bands (hyperspectral reflectance), presumably enabling the efficient classification of pesticide contamination in bee carcasses using artificial intelligence (AI) models, such as machine learning. In this study, bee contamination by commercial formulations of three insecticides-dimethoate (organophosphate), fipronil (phenylpyrazole), and imidacloprid (neonicotinoid)-as well as glyphosate, the most widely used herbicide globally, was detected using machine learning models. These models classified the hyperspectral reflectance profiles of the body surfaces of contaminated bees. The best-performing model, the linear discriminant analysis, achieved 98 % accuracy in discriminating contamination across species Apis mellifera, Melipona mondury, and Partamona helleri, with prediction speeds of 0.27 s. Our pioneering study introduced an effective method for discerning multiple classes of bees contaminated with pesticides using hyperspectral reflectance. An AI-driven spectral data analysis tool (https://github.com/bernardesrodrigoc/MACSS) was developed for the purpose of identifying and characterizing new samples through their spectral characteristics. This platform aids efforts to monitor and conserve bee populations and holds potential importance in environmental monitoring, agricultural research, and industrial quality control.
农药中毒不断威胁着蜜蜂，因为它们在喷洒农药的作物中觅食。这种中毒需要进行彻底的调查，以确定其原因，这突显出可靠的农药检测方法对蜜蜂监测的重要性。红外光谱提供了数百个光谱波段（高光谱反射率）的反射数据，这可能使使用人工智能（AI）模型（如机器学习）来高效地对蜜蜂尸体中的农药污染进行分类。在这项研究中，使用机器学习模型检测了三种杀虫剂（有机磷杀虫剂乐果、苯并吡唑类杀虫剂氟虫腈和烟碱类杀虫剂吡虫啉）以及全球使用最广泛的除草剂草甘膦对商业制剂的蜜蜂污染。这些模型对受污染蜜蜂的体表面的高光谱反射率图谱进行了分类。表现最好的模型是线性判别分析，在识别不同物种（蜜蜂属 Apis mellifera、黑小蜜蜂 Melipona mondury 和无刺蜂属 Partamona helleri）的污染方面达到了 98%的准确率，预测速度为 0.27 秒。我们的开创性研究引入了一种使用高光谱反射率来辨别多种受农药污染的蜜蜂的有效方法。开发了一个基于人工智能的光谱数据分析工具（https://github.com/bernardesrodrigoc/MACSS），用于通过其光谱特征识别和表征新样本。该平台有助于监测和保护蜜蜂种群的工作，并在环境监测、农业研究和工业质量控制方面具有重要意义。
