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Resolving the identification of weak‐flying insects during flight: a coupling between rigorous data processing and biology

ABSTRACT: Abstract Bioacoustic methods play an increasingly important role for the detection of insects in a range of surveillance and monitoring programmes. Weak‐flying insects evade detection because they do not yield sufficient audio information to capture wingbeat and harmonic frequencies. These inaudible insects often pose a significant threat to food security as pests of key agricultural crops worldwide. Automatic detection of such insects is crucial to the future of crop protection by providing critical information to assess the risk to a crop and the need for preventative measures. We describe an experimental set‐up designed to derive audio recordings from a range of weak‐flying aphids and beetles using an LED array. A rigorous data processing pipeline was developed to extract meaningful features, linked to morphological characteristics, from the audio and harmonic series for six aphid and two beetle species. An ensemble of over 50 bioacoustic parameters was used to achieve species discrimination with a success rate of 80%. The inclusion of the dominant and fundamental frequencies improved prediction between beetles and aphids because of large differences in wingbeat frequencies. At the species level, error rates were minimized when harmonic features were supplemented by features indicative of differences in species' flight energies. Data processing pipeline for the automatic identifcation of weak‐flying insects. Insects fly through an opto‐acoustic sensor triggering an audio recording to be made of the sound of their flight. Data are then processed to enable feature extraction. These features are fed into classification algorithms, namely random forests, which are then checked for validity before being sent to the end user for interpretation.

SUBMITTER: Hassall K

PROVIDER: S-EPMC8596709 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.ResultsIn this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.ConclusionWe demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.

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Resolving the identification of weak‐flying insects during flight: a coupling between rigorous data processing and biology

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