Start a conversation ↗Turning environmental data into agricultural intelligence
Working with real-time SEPA sensor data from drought-risk catchments in Scotland, Tekh explored how AI and machine learning could turn environmental monitoring into predictive insight for farmers and water management.
Problem
Tekh worked with environmental data from the Scottish Environment Protection Agency's distributed sensor network across two water catchments near Dundee selected for their drought risk.
Low-cost LoRaWAN sensors captured seven environmental measures including river level, soil moisture, soil conductivity, rainfall, air humidity, temperature and pressure, with data available through the cloud in real time.
The network was already helping farmers manage water abstraction and respond to conditions associated with costly fungal disease. Tekh explored how machine learning and predictive analysis could take this further, using the data to move from understanding current conditions towards anticipating what could happen next.
What we did
We developed an initial demonstrator showing how complex environmental data could be brought together and translated into accessible intelligence for agricultural users.
Result
The work demonstrated the potential for AI to extract greater value from existing environmental infrastructure, supporting better-informed farming decisions while helping protect water bodies from excessive abstraction.
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