I’m building a smart irrigation setup that links a Raspberry Pi edge server with several ESP32 nodes in the field. Each ESP32 gathers data from soil-moisture probes, compact weather boards (temperature, humidity, barometric pressure), and inline flow meters, then reports everything wirelessly to the Pi for processing. Here’s what I need from you: • Python (Raspberry Pi) and MicroPython/C++ (ESP32) code that ingests the raw sensor streams, pushes them through an on-device model, and decides—within seconds—whether to start or stop the main pump and which solenoid valves to open. • An ML pipeline: training notebooks, a lightweight model (TensorFlow Lite or similar) and the inference wrapper that runs locally. The model must act on current soil-moisture readings and short-term weather data, while also generating three forward-looking insights: predicted soil moisture over the next 6–24 h, likely weather changes in that window, and the water volume the system will probably consume. • Control logic that blends those predictions with simple rule-based safeguards (e.g., prevent watering during rain, pause if flow sensor detects anomalies). • MQTT or HTTP messaging between ESP32 nodes and the Pi, with basic encryption, plus a clear JSON schema so I can expand the network later. • Deployment scripts, pinout diagrams, and concise documentation that let me flash a fresh ESP32, drop it in the field, and see it register instantly on the Pi dashboard. Acceptance criteria 1. End-to-end simulation (sensor emulators are fine) proves that the pump and valves react correctly to real-time data and predictive outputs. 2. Model inference on the Pi completes in <500 ms on a single core. 3. Repository contains clean, commented code, README, and a quick-start guide. If the stack you prefer differs slightly—say you’d rather use Edge Impulse or TinyML instead of pure TensorFlow Lite—that’s fine as long as it stays lightweight and runs offline on the Pi. Incorprating Blynk or other visual iot readings is appreciated