  Deep learning provides the opportunity to improve upon conflicting reports
considering the relationship between the Amazon river's fish and dolphin
abundance and reduced canopy cover as a result of deforestation. Current
methods of fish and dolphin abundance estimates are performed by on-site
sampling using visual and capture/release strategies. We propose a novel
approach to calculating fish abundance using deep learning for fish and dolphin
estimates from sonar images taken from the back of a trolling boat. We consider
a data set of 143 images ranging from 0-34 fish, and 0-3 dolphins provided by
the Fund Amazonia research group. To overcome the data limitation, we test the
capabilities of data augmentation on an unconventional 15/85 training/testing
split. Using 20 training images, we simulate a gradient of data up to 25,000
images using augmented backgrounds and randomly placed/rotation cropped fish
and dolphin taken from the training set. We then train four multitask network
architectures: DenseNet201, InceptionNetV2, Xception, and MobileNetV2 to
predict fish and dolphin numbers using two function approximation methods:
regression and classification. For regression, Densenet201 performed best for
fish and Xception best for dolphin with mean squared errors of 2.11 and 0.133
respectively. For classification, InceptionResNetV2 performed best for fish and
MobileNetV2 best for dolphins with a mean error of 2.07 and 0.245 respectively.
Considering the 123 testing images, our results show the success of data
simulation for limited sonar data sets. We find DenseNet201 is able to identify
dolphins after approximately 5000 training images, while fish required the full
25,000. Our method can be used to lower costs and expedite the data analysis of
fish and dolphin abundance to real-time along the Amazon river and river
systems worldwide.
