TY - JOUR
T1 - Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing
AU - Wang, Biwei
AU - Mao, Yuan
AU - Ashry, Islam
AU - Al-Fehaid, Yousef
AU - Al-Shawaf, Abdulmoneim
AU - Ng, Tien Khee
AU - Yu, Changyuan
AU - Ooi, Boon S.
N1 - KAUST Repository Item: Exported on 2021-03-01
Acknowledged KAUST grant number(s): REI/1/4247-01-01
Acknowledgements: This research was funded by KAUST-Research Translation Funding (REI/1/4247-01-01).
PY - 2021/2/25
Y1 - 2021/2/25
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/667692
UR - https://www.mdpi.com/1424-8220/21/5/1592
U2 - 10.3390/s21051592
DO - 10.3390/s21051592
M3 - Article
C2 - 33668776
VL - 21
SP - 1592
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 5
ER -