Data scaling performance on various machine learning algorithms to identify abalone sex

This study aims to analyze the performance of machine learning algorithms with the data scaling process to show the method's effectiveness. It uses min-max (normalization) and zero-mean (standardization) data scaling techniques in the abalone dataset. The stages carried out in this study included data normalization on the data of abalone physical measurement features. The model evaluation was carried out using k-fold cross-validation with the number of k-fold 10. Abalone datasets were normalized in machine learning algorithms: Random Forest, Naïve Bayesian, Decision Tree, and SVM (RBF kernels and linear kernels). The eight features of the abalone dataset show that machine learning algorithms did not too influence data scaling. There is an increase in the performance of SVM, while Random Forest decreases when the abalone dataset is applied to data scaling. Random Forest has the highest average balanced accuracy (74.87%) without data scaling

Hammerhead shark detection using regions with convolutional neural networks (faster R-CNN)

Over the years the illegal catch of sharks in the Pacific Ocean has drastically increased, to the point where the Scalloped Hammerhead Shark has become an endangered species. The monitoring of these for this endangered species is a very difficult procedure due to the fact that most of the methods used for this process are invasive. Given this circumstances marine biologists have chosen as solution the use of underwater cameras to make this analysis directly from videos, but this is still a slow and expensive process...A lo largo de los años, la captura ilegal de tiburones en el Océano Pacífico ha aumentado drásticamente, hasta el punto en que el tiburón martillo se ha convertido en una especie en peligro de extinción. El monitoreo para esta especie es un procedimiento bastante complicado debido a que la mayoría de los métodos utilizados para este proceso son invasivos. Dadas estas circunstancias, los biólogos marinos optaron como solución usar cámaras subacuáticas para hacer este análisis directamente de los videos, pero este sigue siendo un proceso lento y costoso..

Shark Tracking Using Deep Learning

Scalloped hammerhead sharks (Sphyrna lewini) were recently classed as Critically Endangered on the IUCN Red List. Despite global declines, there is a lack of information on the status of this species in the Eastern Tropical Pacific, partly due to inconsistent fisheries-independent monitoring. The use of video footage can be a valuable tool to develop                             standardized indicators, yet analysis of footage can be highly laborious. In this study, we propose a new automated method based on deep convolutional neural networks to detect and track endangered hammerhead sharks in video sequences...El tiburón martillo festoneado (Sphyrna lewini) fue clasificado recientemente como una especie en peligro crítico en la lista roja de la UICN. A pesar de las disminuciones mundiales, hay una falta de información sobre la situación de esta especie en el Pacífico oriental tropical, en parte debido a la falta de una vigilancia independiente de las pesquerías. El uso de material de vídeo puede ser una herramienta valiosa para desarrollar indicadores estandarizados, pero el análisis de las imágenes puede ser muy laborioso..

AquaVision : AI-powered marine species identification

This study addresses the challenge of accurately identifying fish species by using machine learning and image classification techniques. The primary aim is to develop an innovative algorithm that can dynamically identify the most common (within Maltese coastal waters) invasive Mediterranean fish species based on available images. In particular, these include Fistularia commersonii, Lobotes surinamensis, Pomadasys incisus, Siganus luridus, and Stephanolepis diaspros, which have been adopted as this study’s target species. Through the use of machine-learning models and transfer learning, the proposed solution seeks to enable precise, on-the-spot species recognition. The methodology involved collecting and organising images as well as training the models with consistent datasets to ensure comparable results. After trying a number of models, ResNet18 was found to be the most accurate and reliable, with YOLO v8 following closely behind. While the performance of YOLO was reasonably good, it exhibited less consistency in its results. These results underline the potential of the developed algorithm to significantly aid marine biology research, including citizen science initiatives, and promote environmental management efforts through accurate fish species identification.peer-reviewe

Achieving sustainable development goals coupling earth observation data with machine learning

Tese de Doutoramento em Engenharia e Gestão Industrial, Universidade Lusíada, Vila Nova de Famalicão, 2021Exame público realizado em 09 de Junho de 2022The main purpose of this work is to assess and understand the achievement of Sustainable Development Goals by means of Earth Observation (EO) data and Machine Learning (ML) technologies. Thus, this study intends to promote and suggest the use of EO and ML in benefits to the Sustainable Development Goals (SDGs) to support and optimize the actual industry and field processes and moreover provide new insights (techniques) on EO approaches and applicability as well as ML techniques. A review on the Sustainable Development concept and its goals is presented followed by EO data and methods and its approaches relevant to this field, giving special attention to the contribution of ML methods and algorithms as well as their potential and capabilities to support the achievement of SDGs. Additionally, different ML approaches and techniques are reviewed (i.e., Classification and Regression techniques, Non-Binary Decision Tree (NBDT), and two novel methods are proposed, designated as: Random Forest built based on the Non-Binary Decision Tree (NBRF) and Fusion of techniques). Both developed methods are applied, optimized and validated to two case studies also aligned with specific SGDs: Case study I – Identification and mapping of healthy or infected crops, tackling SDGs 2, 8, 9 and 12; and Case study II - Deep-sea mining exploitation SDGs 8, 9, 12 and 14). Such is achieved by using data provided by European satellites or programs that allows to also contribute to the goals for Europe’s Space strategy. For Case study I, the parameters analysed to achieve the respective SDGs correspond to: several vegetation indices as well as the values of the spectral bands. Such parameters have been extracted by means of EO data (from Sentinel-2) and validated with different ML approaches. The results obtained from the different ML approaches suggest that for Case study I, the best classification technique (overall accuracy of 92.87%) as well as the best regression (Root mean square error of 0.148) corresponds to the Fusion of techniques All the applied techniques, however, show their applicability on this case study with good results, disregarding the NBDT which is the “weakest” one (best result on all tests: accuracy of 57.07%). For Case study II, the parameters analysed to achieve the respective SDGs correspond to the topography of the seafloor and, physical and biogeochemical ocean’s parameters. Such parameters have been extracted by means of EO data (from CMEMS and GEBCO) and validated with different ML approaches. The results of these approaches suggests that the best technique corresponds to the Fusion of techniques with a root mean square error of 0.196. However, not all the techniques proved to be appropriated, where the NBDT present the worst results (best result on all tests: accuracy 60.62%). Overall, it is observed that EO plays a key role in the monitoring and achievement of the SDGs given its cost-effectiveness pertaining to data acquisition on all scales and information richness, and the success of ML upon EO data analysis. The applicability of ML techniques allied to EO data has proven, by both case studies, that can contribute to the SDGs and can be extrapolated to other applications and fields. Keywords: Sustainable Development Goals; Earth Observation; Europe Space Strategy; Machine Learning; Deep-sea Mining; Agriculture

Deep learning for marine species recognition

Research on marine species recognition is an important part of the actions for the protection of the ocean environment. It is also an under-exploited application area in the computer vision community. However, with the developments of deep learning, there has been an increasing interest about this topic. In this chapter, we present a comprehensive review of the computer vision techniques for marine species recognition, mainly from the perspectives of both classification and detection. In particular, we focus on capturing the evolution of various deep learning techniques in this area. We further compare the contemporary deep learning techniques with traditional machine learning techniques, and discuss the complementary issues between these two approaches. This chapter examines the attributes and challenges of a number of popular marine species datasets (which involve coral, kelp, plankton and fish) on recognition tasks. In the end, we highlight a few potential future application areas of deep learning in marine image analysis such as segmentation and enhancement of image quality