Image-Based Insect Counting Embedded in E-Traps That Learn without Manual Image Annotation and Self-Dispose Captured Insects

This study describes the development of an image-based insect trap diverging from the plug-in camera insect trap paradigm in that (a) it does not require manual annotation of images to learn how to count targeted pests, and (b) it self-disposes the captured insects, and therefore is suit- able for long-term deployment. The device consists of an imaging sensor integrated with Raspberry Pi microcontroller units with embedded deep learning algorithms that count agricultural pests in- side a pheromone-based funnel trap. The device also receives commands from the server, which configures its operation, while an embedded servomotor can automatically rotate the detached bot- tom of the bucket to dispose of dehydrated insects as they begin to pile up. Therefore, it completely overcomes a major limitation of camera-based insect traps: the inevitable overlap and occlusion caused by the decay and layering of insects during long-term operation, thus extending the auton- omous operational capability. We study cases that are underrepresented in the literature such as counting in situations of congestion and significant debris using crowd counting algorithms en- countered in human surveillance. Finally, we perform comparative analysis of the results from dif- ferent deep learning approaches (YOLOv7/8, crowd counting, deep learning regression). Interest- ingly, there is no one optimal clear-cut counting approach that can cover all situations involving small and large insects with overlap. By weighting the pros and cons we suggest that YOLOv7/8 provides the best embedded solution in general. We open-source the code and a large database of Lepidopteran plant pests.