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.