Demand for ocean-based high-quality and sustainable fish protein soared in the last decade. Unlike precision agriculture, aquaculture remains an under-equipped farming activity. The aquaculture industry has provided remarkable contributions to the Sustainable Development Goal of zero hunger based on providing animal-based protein for human consumption worldwide. The success of the aquaculture industry hinges on appropriate monitoring of key water quality indicators to ensure both animal health and optimal productivity. In this context, the present work presents a cloud-based LoRaWAN system for quasi-real-time tracking of essential water quality parameters by integrating Internet of Things (IoT) sensor devices. The proposed approach harnesses the power of Long Range (LoRa) technology - especially the LoRa Wide Area Network (LoRaWAN) protocol - to facilitate efficient, large-scale monitoring focusing on data security and scalability. With practical insights drawn from IoT system deployment at an industrially relevant aquaculture farm in Brazil, this research provides a comprehensive look into the system's capabilities, drawbacks, and end-user feedback, offering a blueprint for future aquaculture innovations.

Massive garbage patches within all oceanic gyres have garnered global attention, underscoring microplastic pollution as an emerging concern. Various isolated approaches have been proposed for real-time monitoring of microplastics in environmental and industrial settings. However, these fragmented solutions may hinder distributed data and knowledge sharing across applications, limiting the potential for leveraging AI development to enhance early warning systems and decision-making in large-scale industrial operations. To address these challenges, this paper introduces a framework that utilizes Planetary Digital Twins (PDTs) and affordable modular flow cytometry tools. These innovations enable the real-time tracking and sharing of data on microplastic pollution, tracing their origins from polymer-based industrial processes to their presence in the environment. The proposed framework includes a distributed system architecture based on publisher/subscriber for robust and scalable data sharing. By integrating Industry 5.0 principles, which prioritize sustainability and resilient production processes, the digital twin technology enables a dynamic and interconnected monitoring network. The validation results suggest that the proposed system has the potential to expand and enhance environmental risk assessments on a global scale and support the development of mitigation strategies through improved data integration and sharing capabilities for microplastic monitoring.

Fisheries and aquaculture industries notably contribute to animal-source protein production worldwide. Climate change is creating environmental conditions suitable for harmful algal blooms (HAB) on a global scale. Some phytoplankton species can also release toxins, which may cause large-scale marine mortality with knock-on effects on coastal economies. Reliable phytoplankton monitoring and early HAB detection are also essential in climate-resilient solutions for aquaculture applications. Currently, phytoplankton monitoring is primarily based on traditional microscopy. However, it is time-consuming and requires an experienced taxonomist. There is a need to expedite and automate phytoplankton monitoring to support aquaculture industries. Analytical instruments based on microscopy coupled with artificial intelligence (AI) models may be vital to monitoring applications. Digital plankton data sets are usually imbalanced and reflect natural environmental differences. The lack of data to represent minority species/genera prevents AI models from understanding some taxa completely. It compromises system reliability for HAB monitoring applications. The present study investigates state-of-the-art models for class imbalance problems tailored for HAB monitoring within multi-trophic aquaculture farms from Brazil, South Africa, and Scotland. A unified benchmark database covering publicly available microscopic image-based datasets supported phytoplankton modelling. AI deep collaborative models and threshold moving techniques provided the best results compared to standard architectures. It prevailed, especially for low-abundant yet toxic organisms.

Discharges of treated industrial wastewater may impair the receiving surface water quality. Biological early warning systems (biosensors) for continuous holistic water quality monitoring may better tackle the wide range of potential threats from industrial activities (e.g. oxygen depletion, metal traces, chemical toxins). Commercial biosensor solutions based on mussels and oysters behavioural assessment have enabled overall water quality monitoring of industrial effluents. Although burrowing clams present worldwide ecological and economic importance and their behavioural changes are potential indicators of concerning environmental conditions, current technologies do not allow their use as biosensors. Proposing an experimental monitoring setup and comprehend the behavioural patterns of burrowing clams in different water quality conditions are the first steps towards reliable biosensor solutions for water quality assessment. The present work proposes an vision-based tool to assess clams’ behavioural patterns in different levels of water contamination. It may be basis for building holistic biosensor technology based on clams behavioural assessment for industrial effluent monitoring and early alarm. The proposed system measures the total occupied area by animals through a data acquisition system and data processing pipeline. An off-the-shelf camera setup registers top-view images of the animals inside a container. An image segmentation algorithm properly identify the clams and enables behavioral assessment. System suitability is explored in a case study using the yellow clam Amarilladesma mactroides and DCOIT contaminant. The performance of a Watershed and a machine learning segmentation models are investigated. Obtained results indicate both models can achieve high performance in this task. Behavioural tracking stage enables the use of statistical functions to observe behavioural changes in the animals, which may be proxy to overall water quality condition.

Even though the oil industry importance is notable, oil exploitation may cause impairment of aquatic organisms due to the risk of oil spills. In this context, the development of low-cost and effective aquatic pollution sensors has paramount importance. The present study proposes the association of Nonlinear Autoregressive (NAR) neural network and Exponentially Weighted Moving Average (EWMA) control chart in the behavioral analysis of Perna perna mussels. Bivalve mollusks were instrumented with hall effect sensors and magnets, maintained under controlled environmental conditions and exposed to different concentrations of diesel S-500 Water-Accommodated Fraction. Behavioral data were acquired before (3 days) and along (44 hours) toxicological exposure. NAR neural network was used to forecast the Average Opening Amplitude (AOA) of Perna perna mussels under a non-toxicological environment. It was effective in predicting non-exposed behavior of mussels and allowed to consider individual's adaptive nature. EWMA control chart was employed to evaluate the residues among neural network forecast and experimental AOA. The exposure of bivalves to diesel WAF provided a discrepancy among the predicted and experimental AOA. Hence, EWMA control chart has provided out of control (unpredictable) values throughout the toxicological exposure period. The association of NAR neural networks and EWMA control charts is a potential tool in online monitoring of aquatic environments and considers individual peculiarities of each bivalve which leads to the development of more accurate aquatic pollution biosensors.