In modern aquaculture, the focus is on optimizing production and minimizing environmental impact through the use of recirculating water systems, particularly in outdoor setups. In such systems, maintaining water quality is crucial for sustaining a healthy environment for aquatic life, and challenges arise from instrumentation limitations and delays in laboratory measurements that can impact aquatic animal production. This study aimed to predict key water quality parameters in an outdoor recirculation aquaculture system (RAS) for red tilapia aquaculture, including dissolved oxygen (DO), pH, total ammonia nitrogen (TAN), nitrite nitrogen (NO₂–N), and alkalinity (ALK). Initially, a random forest (RF) model was employed to identify significant factors for predicting each parameter, selecting the top three features from routinely measured parameters on the farm: DO, pH, water temperature (Temp), TAN, NO₂–N, and transparency (Trans). This approach aimed to streamline the analysis by reducing variables and computation time. The selected parameters were then used for prediction, comparing the performance of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM models across different epochs (1000, 3000, and 5000). The results indicated that the CNN–LSTM model at 5000 epochs was effective in predicting DO, TAN, NO₂–N, and ALK, with high R² values (0.815, 0.826, 0.831, and 0.780, respectively). However, pH prediction showed lower efficiency with an R² value of 0.377.

Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia’s weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes.

Prior aquatic animal image classification research focused on distinguishing external features in controlled settings, utilizing either digital cameras or webcams. Identifying visually similar species, like Short mackerel (Rastrelliger brachysoma) and Indian mackerel (Rastrelliger kanagurta), is challenging without specialized knowledge. However, advancements in computer technology have paved the way for leveraging machine learning and deep learning systems to address such challenges. In this study, transfer learning techniques were employed, utilizing established pre-trained models such as ResNet50, Xception, InceptionV3, VGG19, VGG16, and MobileNetV3Small. These models were applied to differentiate between the two species using raw images captured by a smartphone under uncontrolled conditions. The core architecture of the pre-trained models remained unchanged, except for the removal of the final fully connected layer. Instead, a global average pooling layer and two dense layers were appended at the end, comprising 1024 units and by a single unit, respectively. To mitigate overfitting concerns, early stopping was implemented. The results revealed that, among the models assessed, the Xception model exhibited the most promising predictive performance. It achieved the highest average accuracy levels of 0.849 and 0.754 during training and validation, surpassing the other models. Furthermore, fine-tuning the Xception model by extending the number of epochs yielded more impressive outcomes. After 30 epochs of fine-tuning, the Xception model demonstrated optimal performance, reaching an accuracy of 0.843 and displaying a 11.508% improvement in predictions compared to the model without fine-tuning. These findings highlight the efficacy of transfer learning, particularly with the Xception model, in accurately distinguishing visually similar aquatic species using smartphone-captured images, even in uncontrolled conditions.

Accurate monitoring of chlorophyll-a (Chl-a) is essential for managing coastal aquaculture, as Chl-a indicates phytoplankton biomass and water quality. This study developed a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism (Attention) to classify Chl-a using hourly, water quality datasets collected from the GOT001 station in Si Racha Bay, Eastern Gulf of Thailand (2020–2024). A random forest (RF) identified sea surface temperature (SEATEMP), dew point temperature (DEWPOINT), and turbidity (TURB) as the most influential variables, accounting for over 90% of the accuracy. Chl-a concentrations were categorized into ecological groups (low, medium, and high) using quantile-based binning and K-means clustering to support operational classification. Model performance comparison showed that the CNN–BiLSTM model achieved the highest classification accuracy (81.3%), outperforming the CNN–LSTM model (59.7%). However, the addition of the Attention did not enhance predictive performance, likely due to the limited number of key predictive variables and their already high explanatory power. This study highlights the potential of CNN–BiLSTM as a near-real-time classification tool for Chl-a levels in highly variable coastal ecosystems, supporting aquaculture management, early warning of algal blooms or red tides, and water quality risk assessment in the Gulf of Thailand and comparable coastal regions.

Sea cucumbers play a vital role in marine and coastal ecosystems, with some species holding significant economic value. Accurate growth assessment, particularly weight estimation, is crucial for their management and conservation. However, direct measurement poses challenges, as sea cucumbers expel internal fluids when handled, altering their body size and weight. This study evaluates the effectiveness of image analysis combined with ArUco markers to estimate the weight of three economically and ecologically important sea cucumber species found in Thailand: black sea cucumber (Holothuria leucospilota), pink warty sea cucumber (Cercodemas anceps), and sandfish (Holothuria scabra). The proposed method demonstrated high accuracy, with R² values of 0.9699, 0.9774, and 0.9882, respectively. Furthermore, no significant differences (p > 0.05) were observed between traditional hand measurements and image-based assessments, with relative errors of 7.71 ± 4.30% for black sea cucumber, 5.06 ± 3.37% for pink warty sea cucumber, and 4.50 ± 3.23% for sandfish. Unlike deep learning, which requires large datasets and high computation, this simple, cost-effective, and adaptable method highlights the potential of image analysis combined with ArUco markers as a non-invasive and accurate tool for estimating sea cucumber weight. This approach minimizes stress on the animals and can be extended to other aquatic species. However, challenges such as shadows, water turbidity, and the presence of similarly shaped objects near sea cucumbers should be considered when applying this technique in field conditions.