Development of a deep learning model from breeding substrate images: a novel method for estimating the abundance of house fly (Musca domestica L.) larvae

• Published in: Pest management science Vol. 77; no. 12; pp. 5347 - 5355
• Main Authors: Ong, Song‐Quan, Ahmad, Hamdan, Majid, Abdul Hafiz Ab
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.12.2021; Wiley Subscription Services, Inc
• Subjects: Abundance; Breeding; Computer vision; deep convolutional neural networks; Deep learning; Illumination; Larvae; Monitoring; Musca domestica; Object recognition; Pests; population survey; Raspberry Pi; Substrates; Teachable Machine 2.0; Test sets; Training evaluation; transfer learning
• ISSN: 1526-498X; 1526-4998
• DOI: 10.1002/ps.6573

BACKGROUND The application of computer vision and deep learning to pest monitoring has recently received much attention. Although several studies have demonstrated the application of object detection to the number of pests on a substrate, for house flies (Musca domestica L.), in which the larvae were aggregated and overlapped together, the object detection technique was difficult to implement. We demonstrate a novel method for estimating larval abundance by using computer vision on larval breeding substrate, in which the reflective color and topography are affected by the size of the population. RESULTS We demonstrate a method using a web‐based tool to construct a deep learning model and later export the model for deployment. We train the model by using breeding substrate images with different spectra of illumination on known densities of larvae and evaluate the training model in both the test set and field‐collected samples. In general, the model was able to predict the larval abundance by the laboratory‐prepared breeding substrate with 87.56% to 94.10% accuracy, precision, recall, and F‐score on the unseen test set, and white and green illumination performed significantly higher compared to other illuminations. For field samples, the model was able to obtain at least 70% correct predictions by using white and infrared illumination. CONCLUSION Larval abundance can be monitored with computer vision and deep learning, and the monitoring can be improved by using more biochemistry parameters as the predictors and examples of field samples included building a more robust model. © 2021 Society of Chemical Industry. The Process flow of estimating the house fly (Diptera: Muscidae) larval abundance by using breeding substrates with different spectra of illumination. Image acquisition with a customized chamber (A), images of the breeding substrate with different illuminations (B), deep learning model construction (C), abundance prediction at the output layer (D).