A new non-invasive tagging method for leopard coral grouper (Plectropomus leopardus) using deep convolutional neural networks with PDE-based image decomposition

Abstract

External tagging methods can aid in the research of leopard coral grouper (Plectropomus leopardus) in terms of its spatio-temporal behavior at population and individual scales. However, due to the strong exclusion ability and the damage to the body wall of P. leopardus, the retention rate of traditional invasive tagging methods is low. To develop a non-invasive identification method for P. leopardus, we adopted a multiscale image processing method based on matched filters with Gaussian kernels and partial differential equation (PDE) multiscale hierarchical decomposition with the deep convolutional neural network (CNN) models VGG19 and ResNet50 to extract shape and texture image features of individuals. Then based on image features, we used three classifiers Random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP)) for individual recognition on sequential images of P. leopardus captured for 50 days. The PDE, ResNet50 and MLP combination obtained a maximum accuracy of 0.985 ± 0.045 on the test set. For individual temporal tracking recognition, feature extraction and model training were performed using images taken in 1-20 days. The classifier could achieve an accuracy of 0.960 ± 0.049 on the test set consisting of images collected in the periods of 20-50 days. The results show that CNNs with the PDE decomposition can effectively and accurately identify P. leopardus.