Professor at China Agriculatural University | China
Prof. Dr. Dong An has developed a comprehensive research profile in the field of Artificial Neural Networks, integrating Artificial Neural Networks into aquaculture, underwater robotics, and image processing. His expertise in Artificial Neural Networks extends to pattern recognition, intelligent feeding systems, and biomass estimation. The integration of Artificial Neural Networks has driven advancements in automation, improved environmental monitoring, and enhanced accuracy in aquatic systems. His research in Artificial Neural Networks has contributed to the transformation of the aquaculture industry, providing innovative solutions for fish behavior quantification, marine path planning, and real-time detection of structural issues in aquaculture nets. Prof. Dr. Dong An’s dedication to Artificial Neural Networks underscores his commitment to advancing both technological innovation and industrial transformation.
Professional Profile
Scopus Profile
Education
Prof. Dr. Dong An has an extensive academic background that supports his work in Artificial Neural Networks. His studies in electronics, control engineering, and microelectronics have built a foundation for exploring Artificial Neural Networks in advanced agricultural and aquatic technologies. Through structured learning and specialized research in Artificial Neural Networks, he has successfully combined computational intelligence with biological systems. The education of Prof. Dr. Dong An highlights the integration of Artificial Neural Networks into complex problem-solving environments, enabling progress in aquaculture automation and robotics. His academic achievements show the steady evolution of Artificial Neural Networks from theoretical models to practical applications. This educational journey reflects the rigorous development of expertise necessary for advancing Artificial Neural Networks in applied sciences.
Experience
Prof. Dr. Dong An’s professional experience centers around developing and applying Artificial Neural Networks in real-world scenarios. His academic roles have focused on using Artificial Neural Networks in aquaculture, image processing, and underwater robotics. Through research leadership, he has supervised projects employing Artificial Neural Networks to optimize feeding, monitor fish behavior, and detect net damage. His international collaborations have extended the applications of Artificial Neural Networks to global challenges in marine systems. His expertise in Artificial Neural Networks bridges agricultural engineering, electronics, and computational modeling. This professional track demonstrates how Artificial Neural Networks can be translated into industry-ready technologies that transform aquaculture, ensuring improved efficiency, reduced labor costs, and enhanced environmental sustainability through advanced computational intelligence.
Research Interest
Prof. Dr. Dong An’s research interests focus heavily on Artificial Neural Networks. He explores how Artificial Neural Networks can analyze complex underwater imagery, enabling precise detection of marine structures and fish activity. His projects employ Artificial Neural Networks to enhance path planning in underwater robotics, improve intelligent feeding mechanisms, and count fish populations accurately. He also investigates how Artificial Neural Networks can integrate with multimodal data, combining spectroscopy and imaging for seed identification in agriculture. His dedication to Artificial Neural Networks demonstrates a multidisciplinary vision, linking biology, electronics, and computation. These research interests show how Artificial Neural Networks are evolving beyond traditional tasks to play a central role in agricultural automation and marine environmental management.
Award and Honor
Prof. Dr. Dong An has received significant recognition for his contributions to Artificial Neural Networks and related fields. His awards at provincial and ministerial levels highlight impactful research integrating Artificial Neural Networks into aquaculture technology and robotics. He has been honored as a top contributor in advancing rural scientific development, showcasing the societal relevance of Artificial Neural Networks. These honors validate his leadership in applying Artificial Neural Networks to challenges in food production and environmental management. The achievements signify how Artificial Neural Networks contribute to sustainable development, automation, and efficiency in complex biological and industrial systems. Prof. Dr. Dong An’s recognition underscores the growing importance of Artificial Neural Networks across interdisciplinary scientific and engineering communities.
Research Skill
Prof. Dr. Dong An possesses advanced research skills in Artificial Neural Networks, enabling groundbreaking applications in aquaculture and robotics. His skill set includes designing and implementing Artificial Neural Networks for pattern recognition, path planning, and environmental monitoring. He effectively integrates Artificial Neural Networks with spectroscopy, hyperspectral imaging, and machine vision systems, producing robust, automated solutions. His capacity to develop, optimize, and validate Artificial Neural Networks across complex datasets ensures practical relevance and scientific accuracy. His technical skills with Artificial Neural Networks have supported innovations that enhance aquaculture productivity, reduce operational risks, and improve data-driven decision-making. These abilities place him at the forefront of researchers leveraging Artificial Neural Networks to advance both theoretical understanding and industrial implementation.
Publication Top Notes
Title: A joint model for predicting dissolved oxygen in offshore aquaculture enclosures across multiple depths
Journal: Aquaculture
Citations: 0
Year: 2026
Title: A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing
Journal: Communications Biology
Citations: 3
Year: 2025
Title: A convolutional neural network-based lightweight motion deblurring method for autonomous visual target tracking in bionic robotic fish
Journal: Expert Systems with Applications
Citations: 0
Year: 2025
Title: Development and Application of the Coverage Path Planning Based on a Biomimetic Robotic Fish
Journal: Journal of Field Robotics
Citations: 1
Year: 2025
Title: MPMS-SGH: Multi-parameter Multi-step Prediction Model for Solar Greenhouse
Journal: Nongye Jixie Xuebao Transactions of the Chinese Society for Agricultural Machinery
Citations: 0
Year: 2025
Title: Achievement of Fish School Milling Motion Based on Distributed Multi-agent Reinforcement Learning
Journal: Journal of Bionic Engineering
Citations: 0
Year: 2025
Title: Review on Quantitative Methods of Fish School Behaviors
Journal: [Not specified]
Citations: 0
Title: Self-supervised learning-based multi-source spectral fusion for fruit quality evaluation: A case study in mango fruit ripeness prediction
Journal: Information Fusion
Citations: 44
Year: 2025
Title: Unsupervised underwater image restoration via Koschmieder model disentanglement
Journal: Expert Systems with Applications
Citations: 0
Year: 2025
Title: Uniformity and deformation: A benchmark for multi-fish real-time tracking in the farming
Journal: Expert Systems with Applications
Citations: 32
Year: 2025
Conclusion
Prof. Dr. Dong An exemplifies innovation in the deployment of Artificial Neural Networks across agriculture, aquaculture, and robotics. His career demonstrates how Artificial Neural Networks transform industrial practices by increasing automation, enhancing sustainability, and improving precision in biological monitoring. The integration of Artificial Neural Networks into underwater robotics, intelligent feeding, and net safety has created lasting economic and environmental benefits. His academic foundation, professional achievements, and global collaborations prove the value of Artificial Neural Networks in complex, real-world systems. Moving forward, Prof. Dr. Dong An’s continued work with Artificial Neural Networks promises further advances in intelligent systems, reinforcing his position as a leading contributor to the intersection of computation, biology, and engineering.