CNN–Aided Optical Fiber Distributed Acoustic Sensing for Early Detection of Red Palm Weevil: A Field Experiment

Abstract

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.

Submarine optical fiber communication provides an unrealized deep-sea observation network

“…The dolphin sound has higher frequency components with peak power at 1329 Hz, compared with the low-frequency sound of the sea lion at a maximum power of 406 Hz. Such temporal and spectrogram data recorded by the DAS system could be combined with machine learning (ML) 38 to distinguish the sounds of various marine mammals.

Figure 7 Detecting the sounds of marine mammals using the MCF-based DAS.

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Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

“…Such cost-effective sensors can also be widely used in biology and agriculture, where there is a need for distributed acoustic monitoring, and suitability of fiber optic DAS for this has already been confirmed [48]. It is important to note that pest registration [49] and the monitoring of the state of beneficial insects [50] or plant crops [51] are associated precisely with the specific tracking of frequency bands in the signal spectrum, and therefore, the results of this work can be very useful.…”

Enhancing Red Palm Weevil Detection Using Bird Swarm Algorithm With Deep Learning Model

“…Subsequently, this gathered information could be analyzed and stored to function water irrigation pumps as well as support the decisions of experts or cultivators. Artificial intelligence (AI) systems like DL and ML methodologies are presently utilized for analyzing obtained environmental and agricultural data [8]. Crop health observation offers a key component of smart precision agriculture, particularly detecting the infective condition of insect pests in the agricultural region.…”