Temperature and humidity, light, carbon dioxide, soil moisture, EC, pH, and weather data in a greenhouse are often not hard to acquire. Tongwei Smart holds that agricultural IoT produces real value only when the data enters management actions. The real difficulty is this: after the data appears, who looks at it, how they look at it, when it is handled, and how it is related to irrigation and on-site management actions. With only curve charts, and without rules, notification, and execution, data easily becomes decoration that "looks abundant."
In its materials on smart agriculture, Tongwei Smart has set down an application approach that runs from environment monitoring and soil monitoring, through video viewing, to linkage with irrigation and related equipment. Sensors and a small weather station do the sensing. The gateway does aggregation and transmission. The platform does centralized monitoring, alarms, historical records, and viewing on a mobile client. Where site conditions and the project agreement allow, the system can also support automated tasks based on time or on data.
Even so, the core of smart agriculture is still to embed data in the management process. For managers, first determine which indicators most affect the current operation, who sets the thresholds, who receives the alarms, and how the on-site response is recorded. For equipment manufacturers, combine equipment operation, faults, and service records, so as to raise after-sales efficiency. For a park, use a unified ledger and reports across multiple bases, so as to reduce information silos.
This is also a remote-operations way of thinking: acquisition is only the first step, and finding anomalies, assigning responsibility to a person, leaving a record of the action, and ongoing review are what give the system lasting value. Different crops, seasons, and management modes have different patterns. Automatic-control strategy and remote-control permissions need to be confirmed by professional staff on site. One fixed set of rules cannot replace every agricultural decision.
When data can help managers make one fewer trip to the site, find an anomaly earlier, and review one operation more clearly, it has actually taken part in agricultural production and service management.