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More Information About 3D Model :
An Automated Hydroponic Plant Garden Farm with IoT-Enabled Control and Nutrient Management represents an advanced paradigm in controlled environment agriculture (CEA), integrating soilless cultivation techniques with sophisticated digital technologies for optimal plant growth and resource efficiency. This system, often referred to as a smart hydroponic farm or IoT hydroponics, leverages automation, real-time monitoring, and Internet of Things (IoT) connectivity to precisely manage all critical environmental and nutritional parameters for plants grown in a nutrient-rich water solution.

At its core, the system relies on hydroponics, a method of growing plants without soil by providing essential mineral nutrients dissolved in water. Common hydroponic techniques include Nutrient Film Technique (NFT), Deep Water Culture (DWC), Drip Systems, Aeroponics, and Wick Systems. The transition from traditional hydroponics to an auto-controlled, monitor-enabled, IoT-integrated system elevates these methods through:

1. Auto Control and Automation:
This aspect involves the use of sensors and actuators connected to a central control unit (e.g., a microcontroller, PLC, or single-board computer). The system automates routine tasks and environmental adjustments:

  • Nutrient Dosing: Peristaltic pumps or solenoid valves automatically dispense precise amounts of nutrient solutions (A and B, micronutrients) into the water reservoir to maintain optimal Electrical Conductivity (EC), which is a measure of the total dissolved solids and thus nutrient concentration.
  • pH Regulation: pH up/down solutions are automatically added to keep the water's acidity/alkalinity within the ideal range for nutrient absorption by plants, typically between 5.5 and 6.5 for most crops.
  • Water Management: Automated refilling of the reservoir, circulation of nutrient solutions, and potentially scheduled draining and replenishment cycles.
  • Environmental Control: Actuators like fans, heaters, coolers, humidifiers, dehumidifiers, and LED grow lights are automatically adjusted based on sensor data to maintain optimal temperature, humidity, CO2 levels, and light intensity/photoperiod.

    2. Monitoring and Sensing:
    Real-time data acquisition is crucial. A network of sensors continuously collects data on vital parameters:
  • Water Parameters: pH, EC (Electrical Conductivity), water temperature, and water level in the reservoir.
  • Air Parameters: Ambient air temperature, relative humidity, and potentially CO2 concentration.
  • Light Parameters: Photosynthetically Active Radiation (PAR) intensity, spectral quality, and duration.
  • Plant Health: Some advanced systems may incorporate cameras for visual analysis of plant growth and health, or even specialized sensors for plant-specific metrics.

    3. Internet of Things (IoT) Integration:
    The IoT component connects the local control system and its sensors/actuators to a broader network, typically the internet. This enables:
  • Remote Monitoring: Growers can access real-time and historical data from their farm via a web-based dashboard or mobile application from anywhere in the world.
  • Remote Control: Parameters can be adjusted, and operations initiated or overridden remotely.
  • Data Logging and Analytics: All sensor data is stored, allowing for historical trend analysis, performance tracking, and identification of anomalies. This data forms the basis for informed decision-making and predictive analytics.
  • Alerts and Notifications: The system can send automated alerts (email, SMS, push notifications) if any parameter deviates from predefined thresholds or if equipment malfunctions.
  • Cloud Connectivity: Data is often processed and stored in cloud platforms, facilitating advanced analytics, machine learning for optimization, and integration with other agricultural management systems.

    4. Nutrient Management:
    Precision nutrient delivery is paramount in hydroponics. The automated system ensures:
  • Dynamic Adjustment: Nutrient concentrations and pH levels are not static but dynamically adjusted based on plant growth stage, species requirements, and real-time consumption rates detected by the EC and pH sensors.
  • Resource Efficiency: By precisely delivering only what is needed, the system minimizes nutrient waste and reduces the environmental impact of runoff.
  • Recipe Management: Different nutrient recipes (concentration ratios of various elements) can be programmed and switched automatically or manually to match the specific needs of various crops or growth phases (e.g., vegetative vs. flowering).

    Benefits:
    These integrated systems offer numerous advantages, including significantly improved resource efficiency (water, nutrients, energy), higher crop yields, faster growth rates, consistent product quality, reduced labor requirements, elimination of soil-borne diseases, and year-round production irrespective of external climate conditions. The data-driven approach allows for continuous optimization and predictive maintenance.

    Applications:
    Automated hydroponic garden farm systems are suitable for a wide range of applications, from small-scale indoor home gardens and urban farming initiatives to large commercial vertical farms and research facilities, addressing challenges such as food security, land scarcity, and climate change resilience.

    KEYWORDS: Hydroponics, Automation, IoT, Smart Farming, Controlled Environment Agriculture, Precision Agriculture, Nutrient Management, Remote Monitoring, Data Analytics, Sensors, Actuators, Plant Growth, EC Control, pH Control, Vertical Farming, Urban Farming, Sustainable Agriculture, Crop Optimization, Environmental Control, Digital Agriculture, Cloud Computing, Predictive Analytics, Resource Efficiency, Hydroponic Nutrients, Soilless Cultivation, Automated Systems, Greenhouse Automation, Internet of Things in Agriculture, Plant Factory, Real-time Data.

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HYDROPONIC PLANTS GARDEN FARM AUTO CONTROL MONITOR IOT NUTRIENT 3D model

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File formats
STL
Stereolithography<br />File Size: 73.2 MB
OBJ
OBJ | 2 files<br />File Size: 155 MB
3DM
Rhinoceros 3D<br />File Size: 135 MB
3DS
3D Studio<br />File Size: 41.7 MB
DAE
Collada<br />File Size: 237 MB
DWG
AutoCAD<br />File Size: 33.2 MB
BLEND
Blender<br />File Size: 133 MB
GLTF
glTF<br />File Size: 42 MB
IGE
IGES<br />File Size: 114 MB
MAX
Autodesk 3ds Max<br />File Size: 259 MB
FBX
Autodesk FBX<br />File Size: 48.9 MB
STP
STEP<br />File Size: 72.8 MB
OTHER
Other<br />File Size: 72.8 MB
SKP
Sketchup<br />File Size: 49.5 MB
SAT
3D ACIS<br />File Size: 126 MB
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