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

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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 suitable 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 inside 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 bottom 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 autonomous 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 encountered in human surveillance. Finally, we perform comparative analysis of the results from different deep learning approaches (YOLOv7/8, crowd counting, deep learning regression). Interestingly, 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.