Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time- and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.

中文翻译：

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owards公司使用机器学习和光纤分布式声学感应技术检测红棕榈象鼻虫

红棕榈象鼻虫（RPW）是有害害虫，已经消灭了全世界许多棕榈树农场。RPW的早期检测具有挑战性，特别是在大型农场中。在这里，我们介绍机器学习和光纤分布式声感测（DAS）技术的组合，作为在广大农场中早期检测RPW的解决方案。在实验室环境中，我们重建了一个农场的条件，其中包括一棵感染了约12天大的象鼻虫幼虫的树和另一棵健康的树。同时，引入了一些噪声源，包括树木周围的风声和鸟声。用光纤DAS系统提供的实验性时域和频域数据进行训练后，完全连接的人工神经网络（ANN）和卷积神经网络（CNN）可以有效地识别具有高分类准确度值的健康树和受侵害树（使用时间数据的ANN占99.9％，使用光谱数据的CNN占99.7％，合理）噪音条件）。这项工作为在包含数千棵树木的露天和大型农场中部署高效，经济高效的光纤DAS监控RPW铺平了道路。