Image-Based Honey Bee Larval Viral and Bacterial Diagnosis Using Machine Learning

Honey bees are essential pollinators of ecosystems and agriculture worldwide. With an estimated 50-80% of crops pollinated by honey bees, they generate approximately $20 billion in market value in the U.S. alone. However, commercial beekeepers often face an uphill battle, losing anywhere from 40-90% of their hives annually, significantly impacted by brood diseases caused by bacterial, viral, and fungal pathogens. Accurate diagnosis of these brood diseases, especially distinguishing bacterial diseases like European Foulbrood (EFB) from viral infections with a superficial resemblance to EFB (EFB-like disease), remains challenging. Incorrect diagnoses often lead to prophylactic antibiotic treatment across entire apiaries, exacerbating antibiotic resistance, disrupting native gut microbiota, and increasing susceptibility to opportunistic pathogens. Correct field diagnosis of brood disease is challenging and requires years of experience to identify and differentiate various disease states according to subtle differences in larval symptomology. To explore the feasibility of an image-based AI diagnosis tool, we collaborated with apiary inspectors and researchers to generate a dataset of 2,759 honey bee larvae images from Michigan apiaries, molecularly verified through 16S rRNA microbiome sequencing and qPCR viral screening. Our dataset included EFB cases and viral infections (ABPV, DWVA, and DWVB), which were augmented to 8,430 and 8,124 images respectively. We leveraged transfer learning techniques, fine-tuning deep convolutional neural networks (ResNet-50v2, ResNet-101v2, InceptionResNet-v2) pre-trained on ImageNet to discriminate between EFB and viral infections. Our proof-of-concept models achieved 73-88% accuracy on the training/validation sets. When tested on an independent dataset from Illinois containing additional viral pathogens not present in training data, the models showed higher accuracy for EFB (72-88%) than viral infections (28-68%), highlighting both the promise and current limitations of this approach. Implementing AI-based diagnostic tools could reduce unnecessary antibiotic treatments and help maintain the microbiome integrity critical to colony health. However, expanding training datasets to include all major pathogens, healthy larvae, and diverse geographic regions will be essential for developing field-ready diagnostic tools.