Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Sensors (Basel). 2021 Feb 25;21(5):1592. doi: 10.3390/s21051592.
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.
红棕榈象鼻虫（RPW）是一种有害的害虫，它已经在全球范围内摧毁了许多棕榈树农场。早期发现 RPW 具有挑战性，特别是在大规模农场中。在这里，我们介绍了将机器学习和光纤分布式声学传感（DAS）技术相结合，作为在大型农场中早期检测 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，其中包括一棵受感染的树，树上有大约 12 天大的象鼻虫幼虫和另一棵健康的树。同时，引入了一些噪声源，包括树木周围的风和鸟叫声。在用光纤 DAS 系统提供的实验时域和频域数据进行训练后，全连接人工神经网络（ANN）和卷积神经网络（CNN）可以有效地识别健康和受感染的树木，具有很高的分类准确率（ANN 用时间数据的准确率为 99.9%，CNN 用频谱数据的准确率为 99.7%，在合理的噪声条件下）。这项工作为在露天和包含数千棵树的大型农场中部署高效、具有成本效益的光纤 DAS 监测 RPW 铺平了道路。
