Smart Greenhouse Solution

1. Solution Background

With the global population growth and the reduction of arable land, traditional farming faces challenges such as low production efficiency, severe resource waste, and weak resilience to risks. Smart greenhouses, by integrating advanced technologies such as IoT, big data, and AI, create a precise and intelligent agricultural production system. This system enables full-dimensional monitoring and regulation of crop growth environments, significantly improving yield and quality while driving agriculture toward resource-saving and environmentally-friendly practices.

2. Solution Overview  

The smart greenhouse solution uses a network of various sensors to collect real-time data on air, soil, and crops. Combined with intelligent control systems and AI algorithms, it automatically adjusts environmental parameters such as temperature, humidity, light, and CO₂ to precisely match crop growth needs. The system covers environmental monitoring, water and fertilizer management, pest and disease early warning, and data-driven decision-making. It can reduce labor dependency by 30%-50%, increase yield by 20%-40%, and save 20%-35% in water and fertilizer resources. 

3. Core Features and Value of the Smart Greenhouse Solution 

3.1 Precise Environmental Control  

- Function: Real-time monitoring and automatic adjustment of temperature, humidity, light, CO₂ concentration, etc., to maintain the optimal growing environment.  

- Value: Shortens crop growth cycle by 10%-15%, improves quality consistency (e.g., sugar content, fruit shape standards). 

3.2 Intelligent Water and Fertilizer Management  

- Function: Based on soil temperature, humidity, EC (electrical conductivity), pH value, and NPK content, triggers precise irrigation and fertilization.  

- Value: Reduces water and fertilizer waste by 30%-50%, decreases soil salinization risk. 

3.3 Pest and Disease Intelligent Control  

- Function: Using leaf surface humidity, environmental data, and image recognition technology, predicts the likelihood of disease occurrence (accuracy >85%).  

- Value: Reduces pesticide use by 25%, decreases pest and disease loss by over 50%. 

3.4 Data-Driven Decision Making  

- Function: Builds crop growth models and provides suggestions for planting cycle optimization and variety breeding.  

- Value: Increases yield per hectare by 15%-25%, supports large-scale cultivation management. 

Atmospheric Temperature Humidity pressure Sensor	illumination sensor	CO2 sensor	Leaf Wetness Sensor
PAR Sensor	3-in-1 Soil Temperature Humidity EC Sensor	Soil Moisture Temperature sensor	Soil pH sensor

4. Smart Greenhouse Sensor System Design and Deployment 

4.1 Air Environment Monitoring  

1. Temperature, Humidity, and Pressure Sensors  

 - Parameters: Temperature (-40~80℃), Humidity (0~100% RH), Pressure (10~1100 hPa).  

 - Deployment: One sensor every 200㎡, hanging 50cm above the crop canopy.  

 - Control: When temperature >30℃, activate cooling curtain; when humidity >85%, open top windows for ventilation.

2. CO₂ Sensor  

 - Range: 0~5000ppm, Accuracy ±50ppm.  

 - Strategy: Maintain CO₂ concentration between 800~1200ppm during daylight, reduce to natural levels at night. 

3. Illuminance & Photosynthetically Active Radiation (PAR) Sensor  

 - Technical Specifications: Illuminance 0~200,000 Lux, PAR 400~700nm wavelength radiation (μmol/m²/s).  

 - Application: When illuminance <15,000 Lux, activate LED supplementary light (red light 660nm + blue light 450nm); adjust shading net opening when PAR value is below crop demand threshold. 

4.2 Soil and Root Monitoring  

1. Soil Temperature, Humidity, and EC Three-in-One Sensor  

 - Parameters: Temperature (-40~80℃), Humidity (0~100%), EC (0~10mS/cm).  

 - Depth: Set according to crop root depth (15cm for leafy vegetables, 30cm for fruiting vegetables).  

 - Control Logic: When EC >3mS/cm, trigger leaching irrigation; when humidity <60%, activate drip irrigation. 

2. Soil pH Sensor  

 - Range: pH 0~14, Accuracy ±0.1.  

 - Management: When pH deviates from optimal range for crops (e.g., tomato pH 6.0~6.8), adjust pH by injecting acidic/alkaline water and fertilizer. 

3. Soil Nitrogen, Phosphorus, and Potassium Sensor  

 - Range: 0-2000mg/kg, Accuracy 1mg/kg. 

4.3 Crop Monitoring  

1. Leaf Surface Humidity Sensor  

 - Installation: Fixed on the back of the crop’s top leaves.  

 - Warning Mechanism: If humidity >90% and temperature >25℃, trigger fan dehumidification to prevent powdery mildew.

5. Smart Greenhouse System Architecture and Workflow 

5.1 Hardware Layer  

- Perception End: Sensors + smart cameras, supporting LoRaWAN/cellular network/Wi-Fi wireless transmission.  

- Execution Devices: Environmental control units (fans, cooling curtains, supplementary lights), water and fertilizer integration machines, pesticide robots. 

5.2 Transmission Layer  

- Communication Protocol: 4G/5G backbone network + LoRaWAN local area network, low data transmission latency.  

- Edge Computing: Gateways have built-in preprocessing algorithms to filter invalid data (e.g., transient light fluctuations). 

5.3 Platform Layer  

- Digital Twin: 3D modeling to reproduce the greenhouse real-time, mapping environmental parameters and device status.  

- AI Engine: LSTM neural network predicts the next 72 hours' environmental trends, with an accuracy rate of >92%. 

5.4 Application Layer  

- Mobile Terminal: Farmer app sends alerts (e.g., “EC value exceeded in zone A3”), supports remote control.  

- Expert System: Contains a crop knowledge base (covering growth models for 50+ common economic crops).

6. Implementation Cases and Results 

Case 1: Plateau Tomato Smart Greenhouse (Qinghai)  

- Challenge: Large diurnal temperature difference (ΔT>25℃), strong UV, and poor soil.  

- Solution:  

- Deploy frost-resistant sensors (normal operation at -30℃), add UV filtering film.  

- Construct step-wise temperature control strategy: 28℃ daytime / 16℃ nighttime, CO₂ enrichment at night.  

- Outcome:  

- Annual yield increased to 45kg/㎡ (traditional greenhouse 25kg/㎡), sugar content reached 8.2 Brix.  

- Water usage reduced by 40%, inspection frequency reduced to once a week. 

Case 2: Butterfly Orchid Tissue Culture Seedling Base (Yunnan)  

- Demand: Precise control of flowering time, tissue culture contamination rate <0.5%.  

- Solution:  

- Deploy pressure gradient sensors in sterile zones to maintain positive pressure environment.  

- Adjustable spectrum LED for light period control (16 hours light/8 hours darkness).  

- Outcome:  

- Flowering time deviation <3 days, tissue culture contamination rate reduced to 0.2%.  

- Energy cost reduced by 35%, annual production of high-quality seedlings reached 5 million. 

7. Technology Evolution Directions  

- Digital Twin Enhancement: Integrating meteorological satellite data for regional greenhouse collaborative control.  

- Robotic Integration: Integrating grafting and harvesting robots with a work efficiency of 200 plants/hour and damage rate <1%.  

- Blockchain Traceability: Linking growth data to blockchain, enabling consumers to scan and check the full lifecycle environmental records. 

Conclusion  

The smart greenhouse solution builds a “perception-analysis-execution” closed-loop system, upgrading agricultural production from experience-driven to data-driven. Its value lies not only in increasing yield and efficiency but also in promoting agricultural carbon neutrality through precise resource management. With the continuous penetration of AIoT technologies, smart greenhouses will become the core carrier of digital agriculture, helping address the dual challenges of global food security and ecological sustainability.