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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 rea...