# Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques
###### Abstract  
Noninvasive morphological feature monitoring is essential in fish culture, since these features are currently measured manually with a high cost. These morphological parameters can concern the size or mass of the fish, or its health as indicated, for example, by the color of the eyes or the gills. Several approaches have been proposed, based either on image processing or machine learning techniques. In this paper, both of these approaches have been combined in a unified environment with novel techniques (e.g., edge or corner detection and pattern stretching) to estimate the fish's relative length, height and the area it occupies in the image. The method can be extended to estimate the absolute dimensions if a pair of cameras is used for obscured or slanted fish. Moreover, important fish parts such as the caudal, spiny and soft dorsal, pelvic and and fins are located. Four species popular in fish cultures have been studied: _Dicentrrachus labrax_ (sea bass), _Diplodus puttazzo, Merluscus merluscus_ (cod fish) and _Spans aurata_ (sea bream). Taking into consideration that there are no large public datasets for the specific species, the training and testing of the developed methods has been performed using 25 photographs per species. The fish length estimation error ranges between 1.9% and 13.2%, which is comparable to the referenced approaches that are trained with much larger datasets and do not offer the full functionality of the proposed method. morphometrics; image processing; deep learning; segmentation; contour; object detection; fish; landmarks; convolutional neural networks +
Footnote †: journal: Applied Sciences  
learning algorithms were used in [3] to analyze pond water for its suitability for fish farming and the causes of fish diseases. The accuracy of these machine learning algorithms was compared in [3]. Other approaches have been proposed to monitor fish trajectories. In [4; 5], fish trajectories and behavior in vertical slot fishways were studied. Computer vision techniques were used to analyze images collected from a calibrated camera. The retrieved images were segmented, subtracting the dynamic background and highlighting the regions where the fish may have existed. Fish rotation was also taken into consideration. Kalman filtering was also employed to estimate the fish trajectory more accurately. In [5], a simple similarity measure was defined to recognize fish with a precision of 97%. [PERSON] et al. also proposed an image processing algorithm based on a noninvasive 3D optical stereo system in [6]. Computer vision techniques have been used to monitor fish in tanks or pools. Some fish biometric parameters, such as their length, are also measured. A system for fish classification operating on the recognized image bounding box is described in [7]. A 2D affine object recognition method is applied using invariant information from the contour and the texture of the fish. Affine invariant features are independent of the object's position, orientation and scale. Texture is extracted from the gray level histogram, Gabor filters and the gray level co-occurrence matrices (GLCM). The classification of a fish image in 1 of the supported 10 species is carried out, with a success rate between 80% and 100%. The distances of 18 landmarks were used in [8] to estimate the fish size and shape in order to classify it into 1 of 20 different fish families. The measured distances were inputs to a three-layer artificial neural network (A-NN). The accuracy of the distance estimations was not reported, but the classification accuracy ranged between 91% and 94%. In a similar approach presented by [PERSON] et al. [9], a generic fish classification evaluation was performed using the memetic algorithm, a genetic algorithm with simulated annealing and backpropagation. Twenty-four poisonous and non-poisonous fish families were tested, and each family consisted of a number of different species. Fish classification was also studied in [9], where an image dataset of six different fish species was exhibited (Fish Pack).