Avenues and means for smart mariculture

Globally, aquaculture is one of the fast-growing production sectors using water productivity concepts. The term aquaculture refers to the practice of farming/ cultivating aquatic organisms that include finfish, shellfish and microscopic and macroscopic plants both in freshwater and saltwater in controlled conditions under human management. Farming/cultivation implies intervention in the rearing process to enhance production, breeding, nursery rearing, stocking, feeding, protection from predators, etc. It also implies individual or corporate ownership, the planning, development and operation of culture systems, sites, facilities and practices, and production and transport. The social and financial significance of aquaculture is growing consistently at >6% in recent years. India has immense potential for aquaculture development, and the sector contributed ≈70% to its total fish production in 2020

Good aquaculture practices and smart aquaculture

Globally, aquaculture stands out as one of the fastest-growing production sectors, employing water productivity concepts. Aquaculture encompasses the practice of cultivating aquatic organisms- finfish, shellfish, other invertebrates and microscopic and macroscopic plants- in controlled conditions under human management, both in freshwater and saltwater. Farming involves interventions such as breeding, nursery rearing, stocking, feeding, and protection from predators. It also includes aspects of individual or corporate ownership, planning, and development, operation of culture systems, sites, facilities, practices, production, and transport. According to the FAO (2014), aquaculture is the fastest-growing animal food sector worldwide, supplying approximately 50% of the fish consumed by humans. In 2020, global aquaculture production reached 122.6 million tons, with 54.4 million tons from inland waters and 68.1 million tons from marine and coastal aquaculture (mariculture), amounting to a total value of about USD 281.5 billion (FAO, 2022). Notably, the Asian region contributed a substantial 91.6% to this production, positioning India as the world’s second-largest aquaculture producer and the third-largest fish producer. The social and financial significance of aquaculture has consistently grown at over 6% in recent years, playing a vital role in global food production and addressing the increasing demand for protein sources, livelihoods, and income. In Afro-Asian countries, where aquaculture plays a vital role in cultural, economic, and nutritional aspects, the adoption of Good Aquaculture Practices and the embrace of Smart Aquaculture technologies become imperative for ensuring long-term food security and sustainable development. Through this lens, this article attempts to uncover the potential of Good Aquaculture Practices and Smart Aquaculture in shaping the future of aquaculture in Afro-Asian countries, striking a balance between economic growth, environmental stewardship, and societal well-being

Development of artificial neural network-based object detection algorithms for low-cost hardware devices

Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers. This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models. The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware

Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review

Fish biomass estimation is one of the most common and important practices in aquaculture. The regular acquisition of fish biomass information has been identified as an urgent need for managers to optimize daily feeding, control stocking densities and ultimately determine the optimal time for harvesting. However, it is difficult to estimate fish biomass without human intervention because fishes are sensitive and move freely in an environment where visibility, lighting and stability are uncontrollable. Until now, fish biomass estimation has been mostly based on manual sampling, which is usually invasive, time‐consuming and laborious. Therefore, it is imperative and highly desirable to develop a noninvasive, rapid and cost‐effective means. Machine vision, acoustics, environmental DNA and resistivity counter provide the possibility of developing nonintrusive, faster and cheaper methods for in situ estimation of fish biomass. This article summarizes the development of these nonintrusive methods for fish biomass estimation over the past three decades and presents their basic concepts and principles. The strengths and weaknesses of each method are analysed and future research directions are also presented. Studies show that the applications of information technology such as advanced sensors and communication technologies have great significance to accelerate the development of new means and techniques for more effective biomass estimation. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Through close cooperation between fisheries experts and engineers, the precision and the level of intelligence for fish biomass estimation will be further improved based on the above methods

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes