Codling moth (CM) is the most devastating global pest of apples with a huge potential impact on the post-harvest quality and yield of the product. Due to the small size of its larvae and potentially hidden behavior, simple visual inspection is ill-suited for accurate infestation detection. The characteristic vibro-acoustic signals of multiple behaviors of CM larvae such as chewing and boring were identified in a previous study. In this study, two different approaches were proposed to build on this previous work: multi-domain feature extraction with machine learning to show basic classification potential, and matched filter-aided classification to show the effects of preprocessing using the larval behavior templates. Additionally, low-intensity heat stimulation was applied to improve classification results by increasing the larvae’s hidden activity rate. The results indicated that the first approach led to accuracies as high as 97.47 % for an acoustic signal duration of 10 s, with heat stimulation improving classification rates to 98.96 % for the same interval. Finally, the matched filter-aided classification approach improved upon the heat stimulated results even further to obtain a 100 % accuracy on classifying the test set for a signal duration of 5 s. These findings suggest that the vibro-acoustic technique can be an adaptable tool for detecting CM infestation in apples and improve post-harvest classification quality in fruit.

苹果蠹蛾 (CM) 是全球最具破坏性的苹果害虫，对产品的收获后质量和产量具有巨大的潜在影响。由于其幼虫的体型小和潜在的隐藏行为，简单的目视检查不适合准确的侵染检测。在先前的研究中确定了 CM 幼虫多种行为的特征振动声信号，例如咀嚼和钻孔。在这项研究中，提出了两种不同的方法来构建以前的工作：使用机器学习进行多域特征提取以显示基本分类潜力，以及匹配过滤辅助分类以显示使用幼虫行为模板进行预处理的效果。此外，应用低强度热刺激通过增加幼虫的隐藏活动率来改善分类结果。结果表明，第一种方法在 10 秒的声信号持续时间内的准确度高达 97.47%，热刺激将相同间隔的分类率提高到 98.96%。最后，匹配过滤辅助分类方法进一步改进了热刺激结果，以在 5 秒的信号持续时间对测试集进行分类时获得 100% 的准确度。这些发现表明，振动声学技术可以成为检测苹果中 CM 侵染和提高水果收获后分类质量的适应性工具。热刺激将相同间隔的分类率提高到 98.96%。最后，匹配过滤辅助分类方法进一步改进了热刺激结果，以在 5 秒的信号持续时间对测试集进行分类时获得 100% 的准确度。这些发现表明，振动声学技术可以成为检测苹果中 CM 侵染和提高水果收获后分类质量的适应性工具。热刺激将相同间隔的分类率提高到 98.96%。最后，匹配过滤辅助分类方法进一步改进了热刺激结果，以在 5 秒的信号持续时间对测试集进行分类时获得 100% 的准确度。这些发现表明，振动声学技术可以成为检测苹果中 CM 侵染和提高水果收获后分类质量的适应性工具。