Overview
The Smart Agriculture System uses soil sensors, weather stations, cameras, irrigation controllers, drones, and other devices to achieve capabilities such as environmental monitoring, automatic irrigation, pest and disease warnings, livestock monitoring, and precision fertilization. Since agricultural scenarios are often in remote areas with limited network coverage, smart agriculture systems commonly employ wireless transmission methods like 4G, NB-IoT, and LoRa, with edge gateways aggregating data from field devices and sending it to the cloud. DJI Agriculture, TopCloud Agriculture, Gigac, and XAG provide agricultural IoT solutions. SimtoLink can provide smart agriculture customers with IoT SIM cards, data pools, NB-IoT connections, and other services to ensure stable networking for field devices.
Differences between smart agriculture and consumer IoT or industrial IoT: devices are distributed sparsely, the environment is harsh (high temperature and humidity, dust), power supply is limited (solar + battery), and network conditions are poor. System design needs to consider low power consumption, offline data continuation, edge caching, and low-cost connectivity.
Core Capabilities
Environmental monitoring
Temperature and humidity, light, soil moisture (including water content), soil EC/pH, CO2 concentration, wind speed and direction, etc. Sensors connect to edge gateways or directly to the cloud via wired or wireless (LoRa, NB-IoT) methods. Data is used for environmental control, irrigation decisions, and growth analysis.
Automatic Irrigation and Precision Control
Automatically control irrigation valves, drip irrigation, and sprinklers based on soil moisture, weather forecasts, and crop water requirements. Supports multiple modes including scheduled, threshold-triggered, and AI-recommended. Integrates with weather data and agricultural knowledge bases to achieve precise irrigation, saving water and fertilizers.
Video and AI
Cameras monitor crop growth, livestock health, and pest occurrences. Combined with AI image recognition, early identification of pests, livestock counting, and growth stage determination can be achieved. Edge AI can reduce video uploads, lowering bandwidth costs.
Data Analysis and Prediction
Growth curves, yield prediction, disaster warnings. By integrating weather, soil, and historical data, it provides a basis for agricultural decision-making. Connected with agricultural technology services and the supply chain, it enables integration of production, supply, and marketing.
Use Cases
Smart greenhouses, precision irrigation, livestock farming (temperature control, feeding, health monitoring), aquaculture (dissolved oxygen, water temperature, feeding). Integrated with weather and agricultural technology service data. IoT SIM cards play a key role in networked field devices, and SimtoLink can provide stable and cost-effective connection solutions.
Field conditions a control loop depends on
The platform schedules irrigation and climate actions
What this software must leave behind is a farm action that can be checked: a valve opening or closing, a fan or wet-curtain adjustment, a fertilizing or feeding suggestion, and the sensor readings at the moment of the action. Curves and pictures are inputs. The action record is the output. A closed loop has at least three steps: a measurement, a decision, and feedback that the action ran. Soil moisture with no valve state means the system can only display. A valve with no flow or pressure readback means the system does not know whether water reached the crop.
Greenhouses, open fields, livestock, and aquaculture do not call for the same measurements. A greenhouse usually watches temperature, humidity, and carbon dioxide. Open fields watch soil moisture, rainfall, and evaporation more closely. Livestock watches the house environment and feed intake. Aquaculture watches dissolved oxygen and water temperature. Forcing one device model onto all of them produces a full set of fields, and none of them can drive an action. Split the model by what is being produced. The shared part keeps only device identity, location, power method, and the time of the report.
Control under intermittent power and intermittent reporting
Automatic control can be switched on when the actuator has its own safe default state, and the site allows the link to enter that default when the connection breaks. A solar node samples by day and sleeps at night, so the cloud sees a sparse series. If a decision requires several points in a row, and the device wakes only a few times a day, the condition never holds, or one normal wake is taken for the process recovering. Write the rule against the largest gap allowed between points. Crossing that gap means only that contact was lost. It does not mean soil moisture is already adequate.
A decision on the edge should be limited to local measurements and local actuators. Decisions that need a forecast, or a comparison across several greenhouses, stay in the cloud, and they may arrive later than the field has changed. While the link is down, the edge runs the last policy that was still valid. After recovery it backfills the action log, and the cloud must not execute old commands inside that log a second time. Where this meets the Edge Rules Engine, local rules cite only quantities the site can read, and cloud policies are pushed by version number, so both sides do not change the same valve at once.
Probe depth and where thresholds come from
A moisture probe in the surface layer and a probe in the root zone represent completely different soil layers. Treating a short swing at the surface as a lack of water in the root zone opens the valve again and again. Conductivity and pH probes have a calibration period. After calibration expires, numbers are still reported as usual. Software that only checks whether a number is present treats drift as a change in the soil. The installation record should store depth, calibration date, and the crop it applies to. Decision logic reads that record, and overdue measurements are downgraded to display only.
Copying a threshold from another crop or another soil is the main source of irrigation that should not have happened. Sandy soil and clay at the same moisture reading correspond to different amounts of available water, and greenhouse crops and open-field grain need water on different rhythms. Bind thresholds to the plot and the crop cycle, and force a review when the crop changes. Image recognition is affected by light and a dirty lens. It can prompt a field walk, and it cannot trigger spraying on its own. Filter video at the edge before sending it up, which cuts traffic and keeps a blurred frame out of recognition. Scenario constraints are in the Smart Agriculture Solutions. This page only defines when the software loop itself counts as closed.