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FWI study uses satellites to monitor aquaculture water quality

July 4, 2024  By Hatchery International staff

A satellite image view of four ARA farms—VVR 1, SKR 1, SBR 1, and KIS 1—participating in the study. This image is representative and not from the actual technology used. (Photo: Fish Welfare Initiative )

Fish Welfare Initiative (FWI) has released the results of a study to assess whether data predicted from satellite imagery could help to detect water quality issues remotely.

FWI collaborated with Captain Fresh, an Indian seafood company using satellite imagery in aquaculture, on the study that involved 20 fish farms, all part of the Alliance for Responsible Aquaculture (ARA) in the Kolleru region of Andhra Pradesh.

It focused on six water quality parameters including ammonia, dissolved oxygen (DO), chlorophyll-a (Chl-a), phycocyanin (PC), pH, and temperature. Data for these parameters were collected empirically (through direct measurements) and remotely (via satellite imagery analysis provided by Captain Fresh). 

For each parameter, the collected data were divided into two groups. One group was used to train a predictive model, while the other was used to validate the model. The data was collected over three weeks.

The researchers assessed the models using four statistical measurements: correlation coefficient (r), coefficient of determination (R²), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Ideal models have r values close to ±1, R² values close to 1, and low RMSE and MAPE values.


For four of the six water quality parameters—ammonia, DO, Chl-a, and PC—predicted and empirical data were sufficiently correlated to suggest that remote monitoring has utility. 

pH and temperature showed no correlation between predicted and empirical data. The r values for these parameters were 0.22 and 0.41, and the R² values were 0.05 and 0.17, indicating a significant deviation of predicted values from actual values. This suggests that remote monitoring of pH and temperature using these models is not currently viable.

The four water quality parameters with high r and R² values, ammonia, DO, Chl-a, and PC also had low RMSE and MAPE values. This implies that the models’ predictions were close to the actual values, supporting the potential utility of remote monitoring for these parameters. In contrast, the high RMSE and MAPE values for pH and temperature further indicate that the models for these parameters are not accurate.

FWI stated that these results are encouraging, indicating real potential to utilize remote monitoring for ammonia, DO, Chl-a, and/or PC via analysis of satellite imagery as part of FWI’s flagship ARA program.

Although this concept will have utility for the ARA, it’s too early to roll it out in the ARA, FWI stated in the results. More work needs to be done to improve the models for predicting the water quality parameters to ensure accuracy and reliability. The models need further training with larger data sets and data collected over a longer period to avoid bias from the short study window.

The researchers said they also need to understand how weather, particularly cloud cover, may impact the accuracy of satellite-predicted water quality values. Nonetheless, they’re excited by the potential of incorporating this technology into their programming to help scale their work and improve the welfare of many more fishes.

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