Enhanced fish bending model for automatic tuna sizing using computer vision

[EN] This paper presents a non-invasive fully automatic procedure to obtain highly accurate fish length estimation in adult Bluefin Tuna, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. The present work takes a geometric tuna model, which was previously developed by the same authors to discriminate fish in 2D images, and proposes new models to enhance the capabilities of the automatic procedure, from fish discrimination to accurate 3D length estimation. Fish length information is an important indicator of the health of wild fish stocks and for predicting biomass using length-weight relations. The proposal pays special attention to parts of the fish silhouette that have special relevance for accurate length estimation. The models have been designed to best fit the rear part of the fish, in particular the caudal peduncle, and a width parameter has been added to better fit the silhouette. Moreover, algorithms have been developed to extract snout tip and caudal peduncle features, allowing better initialization of model parameters. Snout Fork Length (SFL) measurements using the different models are extracted from images recorded with a stereoscopic vision system in a sea cage containing 312 adult Atlantic Bluefin Tuna. The automatic measurements are compared with two ground truths: one configured with semiautomatic measurements of favourable selected samples and one with real SFL measurements of the tuna stock collected at harvesting. Comparison with the semiautomatic measurements demonstrates that the combination of improved geometric models and feature extraction algorithms delivers good results in terms of fish length estimation error (up to 90% of the samples bounded in a 3% error margin) and number of automatic measurements (up to 950 samples out of 1000). When compared with real SFL measurements of the tuna stock, the system provides a high number of automatic detections (up to 6706 in a video of 135¿min duration, i.e., 50 automatic measurements per minute of recording) and highly accurate length measurements, obtaining no statistically significant difference between automatic and real SFL frequency distributions. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the paper.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU). This project has been possible thanks to the collaboration of IEO (Spanish Oceanographic Institute). We acknowledge the assistance provided by the Spanish company Grup Balfego S.L. in supplying boats and divers to acquire underwater video in the Mediterranean Sea.Muñoz-Benavent, P.; Andreu García, G.; Valiente González, JM.; Atienza-Vanacloig, V.; Puig Pons, V.; Espinosa Roselló, V. (2018). Enhanced fish bending model for automatic tuna sizing using computer vision. Computers and Electronics in Agriculture. 150:52-61. https://doi.org/10.1016/j.compag.2018.04.005S526115

Automated artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels

The brine shrimp Artemia, a small crustacean zooplankton organism, is universally used as live prey for larval fish and shrimps in aquaculture. In Artemia studies, it would be highly desired to have access to automated techniques to obtain the length information from Anemia images. However, this problem has so far not been addressed in literature. Moreover, conventional image-based length measurement approaches cannot be readily transferred to measure the Artemia length, due to the distortion of non-rigid bodies, the variation over growth stages and the interference from the antennae and other appendages. To address this problem, we compile a dataset containing 250 images as well as the corresponding label maps of length measuring lines. We propose an automated Anemia length measurement method using U-shaped fully convolutional networks (UNet) and second-order anisotropic Gaussian kernels. For a given Artemia image, the designed UNet model is used to extract a length measuring line structure, and, subsequently, the second-order Gaussian kernels are employed to transform the length measuring line structure into a thin measuring line. For comparison, we also follow conventional fish length measurement approaches and develop a non-learning-based method using mathematical morphology and polynomial curve fitting. We evaluate the proposed method and the competing methods on 100 test images taken from the dataset compiled. Experimental results show that the proposed method can accurately measure the length of Artemia objects in images, obtaining a mean absolute percentage error of 1.16%

Projeto e implementação de um gateway de internet das coisas (IoT) para otimização e monitoramento de processos do agronegócio/ Design and implementation of an internet of things (IoT) gateway for agribusiness process optimization and monitoring

A população global vem crescendo continuamente e juntamente com este crescimento está o aumento da demanda por alimentos para nutri-la e para isto será necessário ampliar a eficiência na produção dos alimentos sendo que uma das opções é usar a tecnologia da informação (TI) para automatizar e otimizar os processos do agronegócio. Com objetivo de facilitar a automatização dos processos da produção rural, neste trabalho é apresentada uma proposta de simplificação por meio da tecnologia da informação seguindo o conceito de internet das coisas (IoT), que faz parte da definição do termo plataformas emergentes. Com a aplicação da IoT no agronegócio é possível implementar ferramentas e mecanismos que proporcionem eficiência ao atendimento das demandas de alimento da crescente população mundial. Neste trabalho são apresentadas também algumas tecnologias utilizadas para implementar o conceito de IoT, algumas áreas da produção rural em que a IoT pode ser aplicada e uma proposta de facilitação da automatização dos processos da produção rural por meio da tecnologia da informação seguindo o conceito de IoT. A aplicação prática da IoT aplicada ao agronegócio apresentada neste trabalho é baseada na plataforma Arduino

Automatic Bluefin Tuna Sizing with a Combined Acoustic and Optical Sensor

[EN] A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor's field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes' ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna's Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU) and PAID-10-19 (UPV).Muñoz-Benavent, P.; Puig Pons, V.; Andreu García, G.; Espinosa Roselló, V.; Atienza-Vanacloig, V.; Pérez Arjona, I. (2020). Automatic Bluefin Tuna Sizing with a Combined Acoustic and Optical Sensor. Sensors. 20(18):1-17. https://doi.org/10.3390/s20185294S117201