PSII-6 Deep Learning image segmentation for extraction of body measurements and prediction of body weight in Nile tilapia

Abstract

Abstract Individual measurement of traits of interest is of great importance for breeding and management decisions in animal production systems. However, measurements are often taken manually, which is laborious and also stressful for the animals. Therefore, the development of fast, precise and indirect measurement methods is paramount. An appealing way for such a task is through computer vision systems (CVS). Hence, the objectives of the current work were: 1) Devise a CVS for autonomous measurement of Nile tilapia body area, length, height, and eccentricity; and 2) Evaluation of linear models for prediction of body weight (BW). The pixels of 822 RGB images of live fish were labeled into background, fish fins or body using the “MTurk” crowdsourcing service. This dataset was then split into training (60% of data) and testing sets for the development of Deep Learning Networks for image segmentation into the three pixel categories. The networks differed in input image size (10 to 40% of original size) and number of encoder/decoder layer stacks (1 to 5). An independent dataset with 831 images was used for validation of the linear predictive models. The results for intersection over union (IoU) show that a network with input of 20% of the original size and 4 encoder/decoder stacks achieved the best results, with IoU on the test dataset of 99, 90 and 64% for background, fish body and fin, respectively (Figure 1). Predicted segmentation from this network on randomly selected images is shown in Figure 2. From the linear models evaluated, the one considering only area as input showed predicted R2 of 0.92 for fish BW (Table 1). In conclusion, the devised CVS was able to correctly separate fish body from background and fins, and a linear model using fish body area as input provided good predictive quality of Nile tilapia BW.