A time series model adapted to multiple environments for recirculating aquaculture systems

Liu, Gedi; Jiang, Yifeng; Zhong, Keyang; Yang, Yan; Wang, Yang

doi:10.1016/j.aquaculture.2023.739284

Cited by 20 publications

(2 citation statements)

References 34 publications

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“…39,40 Compared with the development of advanced models and data augmentation, the integration of effective features can achieve optimal performance with minimal cost. For fusing spatial and temporal information, a multi-graph fusion method based on gated recurrent unit and graph attention neural network has been proposed by Liu et al, 41 showing good adaptability in gathering multidimensional information. To preserve spatial location information of small apples, the decoupled-aggregated module was designed, which can fuse multi-scale features.…”

Section: Introductionmentioning

confidence: 99%

Truss tomato detection under artificial lighting in greenhouse using BiSR_YOLOv5

Yu,

Wang,

Miao

et al. 2024

J. Electron. Imag.

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The visual characteristics of greenhouse-grown tomatoes undergo significant alterations under artificial lighting, presenting substantial challenges in accurately detecting targets. To address the diverse appearances of targets, we propose an improved You Only Look Once Version 5 (YOLOv5) model named BiSR_YOLOv5, incorporating the single-point and regional feature fusion module (SRFM) and the bidirectional spatial pyramid pooling fast (Bi-SPPF) module. In addition, the model adopts SCYLLA-intersection over union loss instead of complete intersection over union loss. Experimental results reveal that the BiSR_YOLOv5 model achieves F1 and mAP@0.5 scores of 0.867 and 0.894, respectively, for detecting truss tomatoes. These scores are 2.36 and 1.82 percentage points higher than those achieved by the baseline YOLOv5 algorithm. Notably, the model maintains a size of 13.8M and achieves real-time performance at 35.1 frames per second. Analysis of detection results for both large and small objects indicates that the Bi-SPPF module, which emphasizes finer feature details, is better suited for detecting small-sized targets. Conversely, the SRFM module, with a larger receptive field, is better suited for detecting larger targets. In summary, the BiSR YOLOv5 test results validate the positive impact of accurate identification on subsequent agricultural operations, such as yield estimation or harvest. This is achieved through the implementation of a simple maturity algorithm that utilizes the process of "finding flaws."

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Section: Introductionmentioning

confidence: 99%

Truss tomato detection under artificial lighting in greenhouse using BiSR_YOLOv5

Yu,

Wang,

Miao

et al. 2024

J. Electron. Imag.

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show abstract

“…In recent years, graph neural network (GNN) models have been implemented to time-series forecasting within the DL field, reaching cutting-edge results [25,26,38]. However, little is known about implementing such a paradigm in the water quality research area, with few works addressing this subject [39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Costa Rocha,

Oliveira Santos,

Van Griensven Thé

et al. 2023

Environments

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Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds.

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Multi-step prediction of greenhouse temperature and humidity based on temporal position attention LSTM

Guo,

Feng

2024

Stoch Environ Res Risk Assess

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A time series model adapted to multiple environments for recirculating aquaculture systems

Cited by 20 publications

References 34 publications

Truss tomato detection under artificial lighting in greenhouse using BiSR_YOLOv5

Truss tomato detection under artificial lighting in greenhouse using BiSR_YOLOv5

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Multi-step prediction of greenhouse temperature and humidity based on temporal position attention LSTM

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