AI-driven aquaculture: A review of technological innovations and their sustainable impacts

Artificial Intelligence in Agriculture, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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A two-stage hybrid model for dissolved oxygen prediction and control in aquaculture

Aquaculture International, Год журнала: 2025, Номер 33(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Energy Optimization in Large-Scale Recirculating Aquaculture Systems: Implementation and Performance Analysis of a Hybrid Deep Learning Approach

Aquacultural Engineering, Год журнала: 2025, Номер unknown, С. 102561 - 102561

Опубликована: Май 1, 2025

Язык: Английский

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A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination

Agronomy, Год журнала: 2025, Номер 15(5), С. 1180 - 1180

Опубликована: Май 13, 2025

Soil pore water electrical conductivity (EC), as a comprehensive indicator of soil nutrient status, is closely linked to crop growth and development. Accurate prediction EC therefore essential for informed scientific management. This study focuses on greenhouse rose cultivation site in Jiangchuan District, Yuxi City, Yunnan Province, China. Leveraging multi-parameter sensors deployed within the facility, we collected continuous data (temperature, moisture, EC, EC) meteorological (air temperature, humidity, vapor pressure deficit) from January December 2024. We propose hybrid model—PSO–CNN–LSTM–BOA–XGBoost (PCLBX)—that integrates particle swarm optimization (PSO)-enhanced convolutional LSTM (CNN–LSTM) with Bayesian algorithm-tuned XGBoost (BOA–XGBoost). The model utilizes highly correlated environmental variables forecast EC. experimental results demonstrate that PCLBX achieves mean square error (MSE) 0.0016, absolute (MAE) 0.0288, coefficient determination (R2) 0.9778. Compared CNN–LSTM model, MSE MAE are reduced by 0.0001 0.0014, respectively, an R2 increase 0.0015. Against BOA–XGBoost yields reduction 0.0006 0.0061 MAE, alongside 0.0077 improvement R2. Furthermore, relative equal-weight ensemble BOA–XGBoost, shows improved performance, decreased 0.0005, increased 0.0007. These underscore superior predictive capability over individual baselines. By enhancing accuracy robustness prediction, this contributes deeper understanding physicochemical dynamics offers scalable tool intelligent perception forecasting. Importantly, it provides agricultural researchers managers deployable generalizable framework digital, precise, management nutrients protected horticulture systems.

Язык: Английский

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Deep Learning for Sustainable Aquaculture: Opportunities and Challenges

Sustainability, Год журнала: 2025, Номер 17(11), С. 5084 - 5084

Опубликована: Июнь 1, 2025

With the rising global demand for aquatic products, aquaculture has become a cornerstone of food security and sustainability. This review comprehensively analyzes application deep learning in sustainable aquaculture, covering key areas such as fish detection counting, growth prediction health monitoring, intelligent feeding systems, water quality forecasting, behavioral stress analysis. The study discusses suitability architectures, including CNNs, RNNs, GANs, Transformers, MobileNet, under complex environments characterized by poor image severe occlusion. It highlights ongoing challenges related to data scarcity, real-time performance, model generalization, cross-domain adaptability. Looking forward, paper outlines future research directions multimodal fusion, edge computing, lightweight design, synthetic generation, digital twin-based virtual farming platforms. Deep is poised drive toward greater intelligence, efficiency,

Язык: Английский

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PM2.5 concentration prediction algorithm integrating traffic congestion index

Journal of Environmental Sciences, Год журнала: 2024, Номер 155, С. 359 - 371

Опубликована: Окт. 10, 2024

Язык: Английский

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