Development of an android app for estimating the water quality parameters in fish pond
2021
Heidari, Sajad | Mirzaee-Ghaleh, Esmaeil | Rabbani, Hekmat | Vesali, Farshad
In this research, a new android app for smartphones for estimating some water quality parameters in carp fish ponds such as pH, electrical conductivity (EC), total dissolved solids (TDS), and turbidity is presented. Contact imaging was used to acquire images from the samples. To estimate pH, EC, TDS, and turbidity values, 12 features were extracted from each image. Features were used as input to the artificial neural network models. The performance of the models was evaluated by the R² and RMSE parameters. Based on the results, the network with a structure of 12-15-4 was selected as the best model. The values of R² for estimating pH, TDS, EC, and turbidity were 0.913, 0.993, 0.994, and 0.958, respectively, while the corresponding values for the RMSE were 0.054, 1.835, 3.766, and 0.262, respectively. Finally, this model was successfully implemented on an app named WaterApp on the android smartphone. For testing the app on the smartphone, the performance of the model was evaluated again using new images. According to the results, the R² values for validation data by the developed WaterApp for pH, EC, TDS, and turbidity were 0.88, 0.913, 0.884, and 0.944, respectively.
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