CHAPTER 12
Chapter 12
Agricultural IoT and Environmental Monitoring
From environmental sensor networks and precision agriculture to LPWAN selection, edge analytics, and AI serving long-running, dispersed agricultural sites.
Where this chapter sits: agricultural IoT is the third industry scenario in the book's progression of intelligent scenarios (after industry in Chapter 10 and the city in Chapter 11). The chapter's core argument is: when the same multi-protocol access framework (LoRa/NB-IoT), edge-cloud collaboration architecture, and AI inference pipeline migrate from the industrial scenario to agriculture, which parts can be reused as-is and which must be re-adapted. This is not a standalone "introduction to agricultural IoT" but a cross-scenario stress test of the technical foundation built over the preceding eleven chapters — sensor selection is swapped (from vibration/current to soil/meteorology), LPWAN becomes the primary communication link (replacing the industrial scenario's Modbus/OPC UA), and edge AI shifts from defect detection to disease identification — while the platform layer's unified data, rule engine, time-series storage, and agent orchestration framework remain unchanged. Readers can read this chapter side by side with the previous two, comparing how the same platform foundation adapts under three differently constrained scenarios (industrial determinism, urban high density, agricultural low power). This reusable platform foundation has a corresponding implementation in the open-source project IoT DC3 (protocol drivers, data center, rule engine), and the migration into agriculture is precisely the validation of the reuse logic "replace only the sensors and the LPWAN driver; keep the platform layer unchanged" — the detailed implementation is covered in Chapter 14's hands-on project.