A novel CNLSTMN based algorithm for predicting dissolved oxygen level of brackish water aquaculture

Abstract India is the third-largest seafood-producing country in the world. Shrimp aquaculture relies heavily on precise water quality monitoring to ensure optimal shrimp health and productivity. Shrimp prices have sharply declined since April 2022. In April 2022, the average price was $10.21 per kilogram. By the latest months of 2024, it has dropped to $6.76 per kilogram. Considering all these factors the research has focused on brackish water aquaculture monitoring. To demonstrate the shortcomings of traditional monitoring methods in brackish water prawn aquaculture, a study was conducted. The investigation revealed that farmers often stay up all night to maintain optimal water quality for healthy shrimp production, as ponds are sensitive to environmental factors such as temperature, pH, and humidity. Implementing an intelligent forecasting approach can address the complexity of crop monitoring issues. A hybrid intelligence mechanism can efficiently forecast and handle large volumes of streaming data. Dissolved Oxygen (DO) levels are critical indicators affecting water salinity and pollutant levels that impacts shrimp health. This study introduces a novel deep learning hybrid algorithm for real-time prediction of dissolved oxygen and quality in shrimp ponds using an IoT-based DO measurement kit. We utilize a uniform DO threshold of ≥5 ppm during the course of the study to determine healthy water conditions for shrimp development. The main objective of the model is to forecast dissolved oxygen (DO) concentration in shrimp ponds, with other environmental factors as secondary objectives. The proposed model integrates CNN and Long Short-Term Memory networks to capture spatial and temporal features of DO data. Results demonstrate the effectiveness of our approach, achieving high prediction accuracy compared to traditional methods. Specifically, our CNLSTMN architecture achieved an MSE of 0.065, RMSE of 0.255, and R 2 of 0.85, significantly outperforming linear regression (MSE: 0.120, RMSE: 0.346, R 2 : 0.65), decision trees (MSE: 0.090, RMSE: 0.300, R 2 : 0.75), and random forests (MSE: 0.075, RMSE: 0.274, R 2 : 0.80). These results demonstrate the superiority of our approach. It provides immediate alerts when DO levels deviate, enhancing environmental monitoring and management practices in shrimp aquaculture. The approach achieves an accuracy of 92.5 %.