Evaluation of BugBox, a software platform for AI-assisted bioinventories of arthropods

Artificial intelligence (AI) technology has the potential to revolutionize entomology and biodiversity research, allowing entomologists to address biodiversity questions on a larger scale than ever before. A new software program, called BugBox, has been developed to facilitate large-scale arthropod bioinventories. BugBox uses an AI algorithm to rapidly classify arthropods from specimen photographs and calculates per-sample diversity indices from its classifications. We evaluated the performance of the AI algorithm over three consecutive training cycles by comparing the AI’s classifications to classifications by an expert human taxonomist. BugBox demonstrated substantial improvement in all test metrics over the three cycles as it was allowed to incorporate the human expert’s corrections into each new model version (e.g., raw accuracy improved from 44% to 78% over the three consecutive model versions). We also used both AI and human data to separately test the hypothesis that regenerative agricultural practices increase arthropod biodiversity in a bioinventory from central North American rangelands. AI classifications were strongly correlated with human identifications, and the AI drew the same conclusion as the human data when comparing diversity indices (Hill numbers): both found evidence that regenerative practices increased arthropod diversity. These results demonstrate that, while the AI was less accurate than the human, it is still able to provide useful surrogate data at scale very rapidly. It can also improve over time under the guidance of human expertise. This technology has profound implications for the scalability of entomological science.

Methods
This data is part of Ecdysis Foundation's 1000 Farms Initiative. Arthropods were collected along transects in agricultural fields, pastures and orchards across multiple states. The specimens were photographed and submitted to the machine-learning software BugBox for classification according to a morphospecies database maintained by Ecdysis Foundation. Submitted data were also reviewed and re-identified by a human expert to document the software's accuracy and to compare biodiversity calculations and hypothesis tests based on AI identifications and human identifications.