We wanted to complement the existing functionality in Cargill's iQuatic app. We aim to aggregate all metrics taken with iQuatic anonymously, and run it through a deep learning model which will predict the survival rate of shrimp. With a trained model, we will be able to manipulate each input individually, and see changes in which metric will result in the biggest increase in survival rate. We can then provide farmers with a list of the most impactful metrics, which is constantly updated as we gather more data. The following are the metrics identified as input:
Dissolved Oxygen Levels (mg/L) Water Temperature (C) Turbidity (10-100) Salinity (ppt) Water pH Ammonio Levels (mg/L) Magnesium (mg/L) Calcium (mg/L) Nitrate (mg/L) Nitrite (mg/L) Potassium (mg/L) Hydrogen Sulfide (mg/L)
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