Soil Condition Monitoring

Farmers can use technology to monitor soil conditions in order to use pesticides and fertilizers more efficiently and reduce negative impacts on the environment. Sensors can collect real-time data on soil moisture, pH, and nutrient content, which can be analyzed by apps or machine learning models to provide valuable insights into soil health.

Applicable Development Board
NuMaker-HMI-MA35D1-S1	1. Object Detection Example: Smart irrigation system Ground cameras or drone cameras capture images of farmland. MA35D1 processes this image data, detecting the dryness and cracks on the soil surface. Based on the detected soil moisture and cracks, the smart irrigation system can automatically adjust the irrigation plan, ensuring that the soil maintains optimal moisture and improves irrigation efficiency. 2. Object Classification Example: Crop growth monitoring Fixed cameras are installed on the farmland, or drones are used for regular巡視, capturing the growth status of crops. MA35D1 processes the image data, classifying the growth status of crops at different stages, such as germination, growth, and maturity. This monitoring helps farm managers to perform precision agriculture operations, such as determining the timing of fertilization and harvesting.
NuMaker-HMI-M467	1. Sensor Fusion Example: Smart agriculture environment monitoring system Multiple sensors, such as soil moisture, pH, temperature, and nutrient content, are deployed on the farmland. Cortex-M4 processes the integrated data from these sensors, comprehensively analyzing the soil environment. The system can provide planting advice based on the data, such as adjusting irrigation time and optimizing fertilization strategies. 2. Anomaly Detection Example: Soil health anomaly alert system Sensors are deployed in different areas of the farmland to continuously monitor the basic parameters of the soil. Cortex-M4 analyzes this data to detect changes that are beyond the normal range, such as excessive acidification or nutrient deficiency. When a soil environment anomaly is detected, the system immediately issues an alert, reminding agricultural producers to take corresponding measures.
NuMaker-IoT-M467	1. Sensor Fusion Example: Smart agriculture environment monitoring system Multiple sensors, such as soil moisture sensors, pH sensors, temperature sensors, and nutrient content sensors, are deployed on the farmland. Cortex-M4 processes the integrated data from these sensors, providing a comprehensive analysis of the soil environment. The system can provide planting advice based on the data, such as adjusting irrigation time and optimizing fertilization strategies. 2. Anomaly Detection Example: Soil health anomaly alert system Sensors are deployed in different areas of the farmland to continuously monitor the basic parameters of the soil. Cortex-M4 analyzes this data to detect changes that are beyond the normal range, such as excessive acidification or nutrient deficiency. When a soil environment anomaly is detected, the system immediately issues an alert, reminding agricultural producers to take corresponding measures.

Applicable Development Board

NuMaker-HMI-MA35D1-S1

1. Object Detection

Example: Smart irrigation system

Ground cameras or drone cameras capture images of farmland.
MA35D1 processes this image data, detecting the dryness and cracks on the soil surface.
Based on the detected soil moisture and cracks, the smart irrigation system can automatically adjust the irrigation plan, ensuring that the soil maintains optimal moisture and improves irrigation efficiency.

2. Object Classification

Example: Crop growth monitoring

Fixed cameras are installed on the farmland, or drones are used for regular巡視, capturing the growth status of crops.
MA35D1 processes the image data, classifying the growth status of crops at different stages, such as germination, growth, and maturity.
This monitoring helps farm managers to perform precision agriculture operations, such as determining the timing of fertilization and harvesting.

NuMaker-HMI-M467

1. Sensor Fusion

Example: Smart agriculture environment monitoring system

Multiple sensors, such as soil moisture, pH, temperature, and nutrient content, are deployed on the farmland.
Cortex-M4 processes the integrated data from these sensors, comprehensively analyzing the soil environment.
The system can provide planting advice based on the data, such as adjusting irrigation time and optimizing fertilization strategies.

2. Anomaly Detection

Example: Soil health anomaly alert system

Sensors are deployed in different areas of the farmland to continuously monitor the basic parameters of the soil.
Cortex-M4 analyzes this data to detect changes that are beyond the normal range, such as excessive acidification or nutrient deficiency.
When a soil environment anomaly is detected, the system immediately issues an alert, reminding agricultural producers to take corresponding measures.

NuMaker-IoT-M467

1. Sensor Fusion

Example: Smart agriculture environment monitoring system

Multiple sensors, such as soil moisture sensors, pH sensors, temperature sensors, and nutrient content sensors, are deployed on the farmland.
Cortex-M4 processes the integrated data from these sensors, providing a comprehensive analysis of the soil environment.
The system can provide planting advice based on the data, such as adjusting irrigation time and optimizing fertilization strategies.

2. Anomaly Detection

Example: Soil health anomaly alert system

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