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.

Fiber optic distributed acoustic sensor (DAS) and distributed temperature sensor (DTS) are considerably important for many applications. It is challenging to design a hybrid DAS-DTS system using the same optical fiber because the operation principles of the two sensors are different. We here deploy the widespread standard multimode fiber (MMF) for simultaneous distributed acoustic and temperature sensing. In particular, we operate the MMF in a quasi-single-mode (QSM) state to simultaneously fulfill the functionality requirements of the DAS and DTS. The reported design offers simultaneous vibration and temperature monitoring with high accuracy. In particular, the DAS has a 12.98 dB mean signal-to-noise ratio (SNR) and the DTS offers temperature measurements with ±1 °C accuracy. This technique is significant for many industrial applications because it efficiently tackles a long-standing issue in practical implementation.

Red palm weevil (RPW) is a harmful pest that destroys many date, coconut, and oil palm plantations worldwide. It is not difficult to apply curative methods to trees infested with RPW; however, the early detection of RPW remains a major challenge, especially on large farms. In a controlled environment and an outdoor farm, we report on the integration of optical fiber distributed acoustic sensing (DAS) and machine learning (ML) for the early detection of true weevil larvae less than three weeks old. Specifically, temporal and spectral data recorded with the DAS system and processed by applying a 100–800 Hz filter are used to train convolutional neural network (CNN) models, which distinguish between “infested” and “healthy” signals with a classification accuracy of ∼97%. In addition, a strict ML-based classification approach is introduced to improve the false alarm performance metric of the system by ∼20%. In a controlled environment experiment, we find that the highest infestation alarm count of infested and healthy trees to be 1131 and 22, respectively, highlighting our system’s ability to distinguish between the infested and healthy trees. On an outdoor farm, in contrast, the acoustic noise produced by wind is a major source of false alarm generation in our system. The best performance of our sensor is obtained when wind speeds are less than 9 mph. In a representative experiment, when wind speeds are less than 9 mph outdoor, the highest infestation alarm count of infested and healthy trees are recorded to be 1622 and 94, respectively.

We experimentally investigated the performance of a distributed acoustic sensor (DAS) designed using a few-mode fiber (FMF), when launching different spatial modes under intentional index perturbation within the fiber. Our demonstration showed that the quasi-single mode (QSM) operated FMF offers higher signal-to-noise ratio (SNR) for the DAS, compared with the case when launching other degenerate higher order modes. Additionally, we compared the behavior of the single-mode fiber (SMF)- and FMF-based DAS when using optical pulses of varying power levels. The FMF enables the realization of a DAS with longer sensing range and higher spatial resolution. The developed FMF-based DAS is further tested via sensing various vibration events produced by piezoelectric transducer (PZT) cylinder, pencil break, and loudspeaker.