A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

Abstract

Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics.

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

“…Digital twin-based intelligent fish farming [130] and two-mode underwater smart sensor objects [131] exemplify the innovative use of IoT and AI/ML in aquaculture. These technologies contribute to real-time monitoring, early disease detection, and efficient resource management.…”

“…The convergence of agriculture with AI and ML technologies offers opportunities for predictive analytics, optimization, and intelligent decision-making. According to several authors, incorporating Generative Adversarial Networks (GANs), hyperparameter optimization, and deep learning techniques can lead to improvements in image conversion, model refinement, and the identification of more efficient learning parameters [94,96,131,148]. It is crucial to continually improve models, which involves updating them after deployment, using meta-heuristic-optimized techniques, and incorporating evolutionary algorithms for hyperparameter optimization [96,148,179].…”

Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation

“…With such algorithms, only acoustic networks can be formed, and the various latencies present at each node can be reduced by roughly 73 percent by using the technique for data collection. Since a non-intrusive form of representation will be left empty, the entire underwater operation can be modified by employing two modes with clever sensor detection processes ( Chang et al, 2022 ). The majority of imaging devices won’t function well in an empty space; hence, sonar representation systems should be used instead of wireless modules.…”

Digital Twin Architecture Evaluation for Intelligent Fish Farm Management Using Modified Analytic Hierarchy Process

“…Digital twins facilitate the implementation of digital aquaculture employing smart technologies to perform data acquisition, analysis, and complex decision-making from fish ponds or sea cages [25,26]. As the fusion of big data, real-time information from the individual farm, and AI, digital twins enhance the efficiency of fish farming, maximize production, reduce cost, and optimize the decision-making process.…”