Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity (2408.14348v2)

Abstract: Large image collections generated from camera traps offer valuable insights into species richness, occupancy, and activity patterns, significantly aiding biodiversity monitoring. However, the manual processing of these datasets is time-consuming, hindering analytical processes. To address this, deep neural networks have been adopted to automate image labelling, but the impact of classification error on ecological metrics remains unclear. Here, we analyse data from camera trap collections in an African savannah (82,300 images, 47 species) and an Asian sub-tropical dry forest (40,308 images, 29 species) to compare ecological metrics derived from expert-generated species identifications with those generated by deep learning classification models. We specifically assess the impact of deep learning model architecture, the proportion of label noise in the training data, and the size of the training dataset on three ecological metrics: species richness, occupancy, and activity patterns. Overall, ecological metrics derived from deep neural networks closely match those calculated from expert labels and remain robust to manipulations in the training pipeline. We found that the choice of deep learning model architecture does not impact ecological metrics, and ecological metrics related to the overall community (species richness, community occupancy) were resilient to up to 10% noise in the training dataset and a 50% reduction in the training dataset size. However, we caution that less common species are disproportionately affected by a reduction in deep neural network accuracy, and this has consequences for species-specific metrics (occupancy, diel activity patterns). To ensure the reliability of their findings, practitioners should prioritize creating large, clean training sets with balanced representation across species over exploring numerous deep learning model architectures.