Investigating generative neural-network models for building pest insect detectors in sticky trap images for the Peruvian horticulture

Pest insects are a problem in horticulture, so early detection is key for their control. Sticky traps are an inexpensive way to obtain insect samples in crops, but identifying them manually is a time-consuming task. Building computational models to identify insect species in sticky trap images is therefore highly desirable. However, this is a challenging task due to the difficulty in getting sizeable sets of training images. In this paper, we studied the usefulness of three neural network generative models to synthesize pest insect images (DCGAN, WGAN, and VAE) for augmenting the training set and thus facilitate the induction of insect detector models. Experiments with images of seven species of pest insects of the Peruvian horticulture showed that the WGAN and VAE models are able to learn to generate images of such species. It was also found that the synthesized images can help to induce YOLOv5 detectors with significant gains in detection performance compared to not using synthesized data. A demo app that integrates the detector models can be accessed through the URL: https://bit.ly/3uXW0Ee