Wing movements underlying sound production in calling, rivalry, and courtship songs of the cricket Gryllus bimaculatus (DeGeer)

A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food-A decision support system

Crickets (Gryllus bimaculatus) produce sounds as a natural means to communicate and convey various behaviors and activities, including mating, feeding, aggression, distress, and more. These vocalizations are intricately linked to prevailing environmental conditions such as temperature and humidity. By accurately monitoring, identifying, and appropriately addressing these behaviors and activities, the farming and production of crickets can be enhanced. This research implemented a decision support system that leverages machine learning (ML) algorithms to decode and classify cricket songs, along with their associated key weather variables (temperature and humidity). Videos capturing cricket behavior and weather variables were recorded. From these videos, sound signals were extracted and classified such as calling, aggression, and courtship. Numerical and image features were extracted from the sound signals and combined with the weather variables. The extracted numerical features, i.e., Mel-Frequency Cepstral Coefficients (MFCC), Linear Frequency Cepstral Coefficients, and chroma, were used to train shallow (support vector machine, k-nearest neighbors, and random forest (RF)) ML algorithms. While image features, i.e., spectrograms, were used to train different state-of-the-art deep ML models, i,e., convolutional neural network architectures (ResNet152V2, VGG16, and EfficientNetB4). In the deep ML category, ResNet152V2 had the best accuracy of 99.42%. The RF algorithm had the best accuracy of 95.63% in the shallow ML category when trained with a combination of MFCC+chroma and after feature selection. In descending order of importance, the top 6 ranked features in the RF algorithm were, namely humidity, temperature, C#, mfcc11, mfcc10, and D. From the selected features, it is notable that temperature and humidity are necessary for growth and metabolic activities in insects. Moreover, the songs produced by certain cricket species naturally align to musical tones such as C# and D as ranked by the algorithm. Using this knowledge, a decision support system was built to guide farmers about the optimal temperature and humidity ranges and interpret the songs (calling, aggression, and courtship) in relation to weather variables. With this information, farmers can put in place suitable measures such as temperature regulation, humidity control, addressing aggressors, and other relevant interventions to minimize or eliminate losses and enhance cricket production.

Genomics and genome editing techniques of crickets, an emerging model insect for biology and food science

Most tools available for manipulating gene function in insects have been developed for holometabolous species. In contrast, functional genetics tools for the Hemimetabola are highly underdeveloped. This is a barrier both to understanding ancestral insect biology, and to optimizing contemporary study and manipulation of particular large hemimetabolous orders of crucial economic and agricultural importance like the Orthoptera. For orthopteran insects, including crickets, the rapid spread of next-generation sequencing technology has made transcriptome data available for a wide variety of species over the past decade. Furthermore, whole genome sequences of orthopteran insects with relatively large genome sizes are now available. With these new genome assemblies and the development of genome editing technologies such as the CRISPR-Cas9 system, it has become possible to create gene knock-out and knock-in strains in orthopteran insects. As a result, orthopteran species should become increasingly feasible for laboratory study not only in research fields that have traditionally used insects, but also in agricultural fields that use them as food and feed. In this review, we summarize these recent advances and their relevance to such applications.

An auditory-responsive interneuron descending from the cricket brain: a new element in the auditory pathway

Crickets receive auditory information from their environment via ears located on the front legs. Ascending interneurons forward auditory activity to the brain, which houses a pattern recognition network for phonotaxis to conspecific calling songs and which controls negative phonotaxis to high-frequency sound pulses. Descending brain neurons, however, which are clearly involved in controlling these behaviors, have not yet been identified. We describe a descending auditory-responsive brain neuron with an arborization pattern that coincides with the ring-like auditory neuropil in the brain formed by the axonal arborizations of ascending and local interneurons, indicating its close link to auditory processing. Spiking activity of this interneuron occurs with a short latency to calling song patterns and the neuron copies the sound pulse pattern. The neuron preferentially responds to short sound pulses, but its activity appears to be independent of the calling song pattern recognition process. It also receives a weaker synaptic input in response to high-frequency pulses, which may contribute to its short latency spiking responses. This interneuron could be a crucial part in the auditory-to-motor transformation of the nervous system and contribute to the motor control of cricket auditory behavior.