Search Results (22)

The need to maximize aquaculture production while addressing environmental and food security challenges posed by climate change has driven research towards the development of functional aquafeeds that enhance performance and immunity in farmed species. However, exposure to dietary and environmental stressors affects marine organisms, altering key metabolic pathways best understood through high-throughput “omics” tools. This study assessed the effects of Asparagopsis taxiformis supplementation on central metabolic pathways by analyzing changes in primary metabolite levels in the liver of farmed Diplodus sargus under optimal and suboptimal temperature conditions. Results showed that seaweed supplementation had a beneficial effect on the fish’s primary metabolome; however, inclusion levels and rearing conditions played a crucial role in determining outcomes. While 1.5% supplementation maintained a balanced primary metabolome under optimal temperature conditions, 3.0% supplementation most effectively mitigated the adverse effects of acute thermal stress during a marine heatwave. These findings highlight the nutritive and functional potential of A. taxiformis supplementation in aquafeeds for marine omnivorous fish species and emphasize the importance of evaluating functional aquafeeds under suboptimal rearing conditions. Overall, our results demonstrate the value of metabolomics in elucidating the molecular basis underlying biological pathways in farmed marine fish and optimizing production through climate-smart dietary strategies. Full article

Smart fisheries, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and image processing, are pivotal in enhancing aquaculture efficiency, sustainability, and resource management by enabling real-time environmental monitoring, precision feeding, and disease prevention. However, underwater fish recognition faces challenges in complex aquatic environments, which hinder accurate detection and behavioral analysis. To address these issues, we propose a novel image instance segmentation framework based on a deep learning neural network, defined as the AASNet (Agricultural Aqua Segmentation Network). In order to improve the accuracy and real-time availability of fine-grained fish recognition, we introduce a Linear Correlation Attention (LCA) mechanism, which uses Pearson correlation coefficients to capture linear correlations between features. This helps resolve inconsistencies caused by lighting changes and color variations, significantly improving the extraction of semantic information for similar objects. Additionally, Dynamic Adaptive Focal Loss (DAFL) is designed to improve classification under extreme data imbalance conditions. Abundant experiments on two underwater datasets demonstrated that the proposed AASNet obtains an optimal balance between segmentation performance and efficiency. Concretely, AASNet achieves mAP scores of 31.7 and 47.4, respectively, on the UIIS and USIS dataset, significantly outperforming existing state-of-the-art methods. Moreover, AASNet achieves an inference image recognition speed of up to 28.9 ms/per, which is suitable for practical agricultural applications of smart fish farming. Full article

With the rapid advancement of intelligent aquaculture, precise individual identification of underwater fish has become a crucial method for achieving smart farming. By accurately recognizing and tracking individuals within the same species, researchers can enable individual-level identification and tracking, significantly enhancing the efficiency of research and management. To address the challenges of complex underwater environments and subtle differences among similar individuals that affect recognition accuracy, this paper proposes a fish individual identification method based on global detail enhancement and keypoint region fusion. This method simultaneously learns global refined features and keypoint region features, dynamically capturing effective keypoint features while mitigating errors caused by noise through weighted fusion. The network first employs a global detail enhancement module to extract global features, such as overall morphology and texture information, followed by the extraction of fine-grained features from keypoint regions. Through the weighted fusion, the network further emphasizes critical areas, thereby enhancing robustness and adaptability in complex underwater scenarios. This design effectively integrates global refined features and local keypoint features, providing comprehensive support for accurate fish individual identification. Experimental results show that the proposed method achieves mAP and Rank-1 scores of 89.7% and 95.3%, respectively, and demonstrates strong generalization capabilities in other fish identification tasks. Full article

Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance fish survival rates across various regions and species setups. The system integrates advanced sensors connected to an ESP32 microcontroller, continuously monitoring key water parameters such as pH, temperature, and turbidity which are increasingly affected by climate-induced variability. A custom-built dataset comprising 43,459 records, covering ten distinct fish species across diverse pond environments, was meticulously curated. The data were stored as a comma-separated values (CSV) file on the IoT cloud platform ThingSpeak and synchronized with Firebase, enabling seamless remote access, control, and real-time updates. Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. Our framework’s capability to manage complex, multi-species fishpond environments was validated across diverse setups, showcasing its potential to transform fish farming practices by ensuring sustainable climate-adaptive management through real-time water quality optimization. This study marks a significant step forward in climate-smart aquaculture, contributing to enhanced fish health, survival, and yield while mitigating the risks posed by climate change on aquatic ecosystems. Full article

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply. Full article

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities. Full article

This paper analyzes the factors influencing households’ resilience capacities to shocks within Kenya’s fisheries and aquaculture sectors and draws from primary data collected from 419 fish-dependent households across Kisumu, Busia, Mombasa, and Kilifi counties. The sample represents a total of 48,000 fishing households. The study adopted a quasi-longitudinal design and computed the household resilience capacity index (RCI) using the resilience index measurement and analysis (RIMA-II) model. The results indicate that male-headed households’ mean household RCI scores (mean = 45.07 ± 10.43) were statistically significant to that of female-headed households (mean = 38.15 ± 9.25), suggesting that female-headed households are associated with lower resilience capacities than male-headed households. Moreover, the study identifies differences in resilience levels across various occupations within the sector. For instance, RCI scores among fish traders (mean = 40.71 ± 9.97), a function performed mainly by women, statistically differed (p < 0.005) from male-dominated cage farming (mean = 48.60 ± 10.47), whereas RCI scores at the production level for fisher folks (mean = 44.89 ± 10.09) and pond farmers (mean = 44.04 ± 12.07) showed no statistical difference (0 > 0.05. Additionally, households with more income sources tend to have higher resilience capacities. Seasonality in fishing cycles limited households’ ability to recover from climate-induced shocks; the more months without fishing activity, the less the odds of recovery from shocks (OR = 0.532, 95% CI [0.163, 0.908], p = 0.022). Furthermore, households that lacked guaranteed market access and inputs during COVID-19 were less likely to recover during and after the shocks (OR = 0.401, 95% CI [0.161, 0.999], p = 0.05). Households organized in cooperatives with better access to credit showed a higher chance of recovery. The study recommends (a) adopting gender-sensitive approaches in fisheries and aquaculture interventions to empower women in trade, (b) strengthening policies to enhance access and adoption of climate-smart technologies such as cage fish farming, (c) promoting livelihood diversification to sustain households’ income during fishing off-seasons, and (d) enhancing market linkages in the fish value chain through coordinated producer organizations. Further research should explore the possibilities of introducing index-based weather insurance and other tested suitable safety nets for the fisheries and aquaculture sector. Full article

The increasing frequency and duration of marine heatwaves (MHWs) due to climate change pose severe threats to aquaculture, causing drastic physiological and growth impairments in farmed fish, undermining their resilience against additional environmental pressures. To ensure sustainable production that meets the global seafood demand and animal welfare standards, cost-effective and eco-friendly strategies are urgently needed. This study explored the efficacy of the red macroalga Asparagopsis taxiformis on juvenile white seabream Diplodus sargus reared under optimal conditions and upon exposure to a MHW. Fish were fed with four experimental diets (0%, 1.5%, 3% or 6% of dried powdered A. taxiformis) for a prophylactic period of 30 days (T30) and subsequently exposed to a Mediterranean category II MHW for 15 days (T53). Biometric data and samples were collected at T30, T53 and T61 (8 days post-MHW recovery), to assess performance indicators, biomarker responses and histopathological alterations. Results showed that A. taxiformis supplementation improved catalase and glutathione S-transferase activities and reduced lipid peroxidation promoted by the MHW, particularly in fish biofortified with 1.5% inclusion level. No histopathological alterations were observed after 30 days. Additionally, fish biofortified with 1.5% A. taxiformis exhibited increased citrate synthase activity and fish supplemented with 1.5% and 3% showed improved digestive enzyme activities (e.g., pepsin and trypsin activities). Overall, the present findings pointed to 1.5% inclusion as the optimal dosage for aquafeeds biofortification with A. taxiformis, and confirmed that this seaweed species is a promising cost-effective ingredient with functional properties and great potential for usage in a climate-smart context. Full article

Projections indicate aquaculture will produce 106 million tonnes of fish by 2030, emphasizing the need for efficient and sustainable practices. New technologies can provide a valuable tool for adequate fish farm management. The aim of this paper is to explore the factors affecting fish well-being, the design of control systems for aquaculture, and the proposal of a smart system based on algorithms to improve efficiency and sustainability. First, we identify the domains affecting fish well-being: the production domain, abiotic domain, biotic domain, and control systems domain. Then, we evaluate the interactions between elements present in each domain to evaluate the key aspects to be monitored. This is conducted for two types of fish farming facilities: cages in the sea and recirculating aquaculture systems. A total of 86 factors have been identified, of which 17 and 32 were selected to be included in monitoring systems for sea cages and recirculating aquaculture systems. Then, a series of algorithms are proposed to optimize fish farming management. We have included predefined control algorithms, energy-efficient algorithms, fault tolerance algorithms, data management algorithms, and a smart control algorithm. The smart control algorithms have been proposed considering all the aforementioned factors, and two scenarios are simulated to evaluate the benefits of the smart control algorithm. In the simulated case, the turbidity when the control algorithm is used represents 12.5% of the turbidity when not used. Their use resulted in a 35% reduction in the energy consumption of the aerator system when the smart control was implemented. Full article

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer’s manual labor. Full article

AI plays a pivotal role in predicting plant growth in agricultural contexts and in creating optimized environments for cultivation. However, unlike agriculture, the application of AI in aquaculture is predominantly focused on diagnosing animal conditions and monitoring them for users. This paper introduces an Automated Fish-feeding System (AFS) based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), aiming to establish an automated system akin to smart farming in the aquaculture sector. The AFS operates by precisely calculating feed rations through two main modules. The Fish Growth Measurement Module (FGMM) utilizes fish data to assess the current growth status of the fish and transmits this information to the Feed Ration Prediction Module (FRPM). The FRPM integrates sensor data from the fish farm, fish growth data, and current feed ration status as time-series data, calculating the increase or decrease rate of ration based on the present fish conditions. This paper automates feed distribution within fish farms through these two modules and verifies the efficiency of automated feed distribution. Simulation results indicate that the FGMM neural network model effectively identifies fish body length with a minor deviation of less than 0.1%, while the FRPM neural network model demonstrates proficiency in predicting ration using a GRU cell with a structured layout of 64 × 48. Full article

This study is related to Smart Aqua Farm, which combines artificial intelligence (AI) and Internet of things (IoT) technology. This study aimed to monitor fish growth in indoor aquaculture while automatically measuring the average size and area in real time. Automatic fish size measurement technology is one of the essential elements for unmanned aquaculture. Under the condition of labor shortage, operators have much fatigue because they use a primitive method that samples the size and weight of fish just before fish shipment and measures them directly by humans. When this kind of process is automated, the operator’s fatigue can be significantly reduced. Above all, after measuring the fish growth, predicting the final fish shipment date is possible by estimating how much feed and time are required until the fish becomes the desired size. In this study, a video camera and a developed light-emitting grid panel were installed in indoor aquaculture to acquire images of fish, and the size measurement of a mock-up fish was implemented using the proposed method. Full article

Thanks to continuously evolving data management solutions, data-driven strategies are considered the main success factor in many domains. These strategies consider data as the backbone, allowing advanced data analytics. However, in the agricultural field, and especially in fish farming, data-driven strategies have yet to be widely adopted. This research paper aims to demystify the situation of the fish farming domain in general by shedding light on big data generated in fish farms. The purpose is to propose a dedicated data lake functional architecture and extend it to a technical architecture to initiate a fish farming data-driven strategy. The research opted for an exploratory study to explore the existing big data technologies and to propose an architecture applicable to the fish farming data-driven strategy. The paper provides a review of how big data technologies offer multiple advantages for decision making and enabling prediction use cases. It also highlights different big data technologies and their use. Finally, the paper presents the proposed architecture to initiate a data-driven strategy in the fish farming domain. Full article

Scientific methods are used to monitor fish growth and behavior and reduce the loss caused by stress and other circumstances. Conventional techniques are time-consuming, labor-intensive, and prone to accidents. Deep learning (DL) technology is rapidly gaining popularity in various fields, including aquaculture. Moving towards smart fish farming necessitates the precise and accurate identification of fish biodiversity. Observing fish behavior in real time is imperative to make better feeding decisions. The proposed study consists of an efficient end-to-end convolutional neural network (CNN) classifying fish behavior into the normal and starvation categories. The performance of the CNN is evaluated by varying the number of fully connected (FC) layers with or without applying max-pooling operation. The accuracy of the detection algorithm is increased by 10% by incorporating three FC layers and max pooling operation. The results demonstrated that the shallow architecture of the CNN model, which employs a max-pooling function with more FC layers, exhibits promising performance and achieves 98% accuracy. The presented system is a novel step in laying the foundation for an automated behavior identification system in modern fish farming. Full article

Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective. Full article