Fast communication: edible cricket detection using transferred machine learning model
AbstractDue to the similarity of most edible cricket species, identifying them in the field or from breeding facilities is more complex than spotting generic specimens. However, it can be done using neural network model to identify and classify cricket species. Since machine learning algorithms are difficult to understand and much more difficult to execute. We seek to solve these laborious processes in this paper by using the most recent machine learning application and their applicability for assisting biodiversity of three cricket species. A graphical user interface bases machine learning application (Lobe) usability for classifying edible insects and their sexes is proposed in this Abstract.