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AI for Good-Innovate for Impact

Use case – 49: AI-enabled Soil Analysis and Weather Station for

Local Farmers 49-CSIR-INSTI

Country: Ghana

Organization: CSIR - Institute for Scientific and Technological Information

Contact person: Dennis Agyemanh Nana Gookyi dennisgookyi@ gmail .com,
+233503493435,
Fortunatus Aabangbio Wulnye fortunatuswulnye@ outlook .com ,
Roger Kwao Ahiadormey rogerkwao@ gmail .com
Michael Wilson yboabengwilson@ gmail .com
Yaw Twum Barimah ytbarimah@ yahoo .com
Paul Danquah pauldanquah@ yahoo .com

49�1� Use case summary table

Domain Agriculture
The problem to be Inaccurate weather and soil condition predictions for local farm-
addressed lands due to reliance on national meteorological data that is not
specific to local areas. This leads to crop losses and reduced yield
for local farmers.

Key aspects of the solu- • Real-time data collection using AI-enabled weather stations
tion and soil analysis sensors.
• Monitoring of rainfall intensity, wind intensity and direction,
temperature, humidity, pressure, and air quality.
• Soil analysis for temperature, humidity, pH, NPK, and conduc-
tivity (EC).
• Data visualization on dashboards and accessibility through
smartphone applications and web browsers.
• Predictive modeling for accurate local farm-specific forecasts.

Technology keywords AI, soil analysis, weather station, real-time data, predictive model-
ing, IoT, sensors
Data availability Privately available

Metadata (type of data) • Environmental data (rainfall, wind, temperature, humidity, pres-
sure, air quality)
• Soil data (temperature, humidity, pH, NPK, conductivity)

Model Training and Using collected data over time to design and refine predictive
fine-tuning models for accurate local forecasts.

Testbeds or pilot deploy- https:// github .com/ ITU -AI -ML -in -5G -Challenge/ ITU -2024
ments -GenStorm -Submission -Next -Gen -TinyML -Smart -Weather -Station

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