Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing

Sensors

Yousef Al-Fehaid¹  Abdulmoneim Al-Shawaf¹  BoonS. Ooi²  Biwei Wang²  Yuan Mao²  Islam Ashry²  TienKhee Ng²  Changyuan Yu³ 

[1] Center of Date Palms and Dates, Ministry of Environment, Water and Agriculture, Al-Hassa 31982, Saudi Arabia;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;

关键词: red palm weevil;    fiber optic acoustic sensing;    machine learning;   

DOI  :  10.3390/s21051592

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown