Research Article
Design and Implementation of Smart Hydroponics Farming Using IoT-Based AI Controller with Mobile Application System
Table 2
Prediction-DLCNN response.
| | Devices | Sample 1 | Sample 2 | Sample 3 |
| | Sensor data | Sunlight | HIGH | LOW | LOW | | Air temperature (°C) | 38 | 30 | 28 | | Water temperature (°C) | 32 | 28 | 30` | | pH | 6 | 8 | 7 | | Turbidity (%) | 80 | 30 | 40 | | NPK | 72 | 65 | 80 | | Predicted nutrients | Nitrogen (mg/kg) | 25 | 26 | 31 | | Phosphorus (mg/kg) | 39 | 40 | 42 | | Potassium (mg/kg) | 41 | 50 | 36 | | Magnesium (mg/kg) | 157 | 142 | 138 | | Sulphur (mg/kg) | 3500 | 3150 | 3420 | | Calcium (mg/kg) | ON | OFF | OFF | | Output actuator action during automatic mode | Fresh water pump | OFF | ON | ON | | Drain water pump | ON | OFF | OFF | | Cooler | ON | ON | OFF | | Motor | HIGH | LOW | LOW |
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