Development of a watering recommendation system based on weather and soil moisture data

Context

Due to climate change, urban green spaces are increasingly affected by drought, which makes maintenance and water consumption a challenge. Targeted irrigation can help to use resources efficiently and have a positive impact on the urban climate. By providing gardeners with watering recommendations, over- and under-irrigation can be avoided, water consumption reduced and the long-term health of plants ensured. This promotes sustainable urban development and conserves valuable water resources.

In collaboration with the city of Rastatt, a system is therefore to be developed that provides gardeners with precise watering recommendations. The aim of the work is to use weather data and soil moisture measurements to create a predictive model that is based on historical data and indicates future irrigation requirements. This should optimize water consumption and enable sustainable irrigation.

Goals

• Research into the state of the art of existing irrigation planning methods and their use in an urban context
• Concept development for soil moisture measurement; identification and selection of suitable sensors and measurement methods.
• Development of a model that provides recommendations for irrigation based on historical weather and soil moisture data.
• Documentation of the work, presentation of the results on site

Requirements

• Interest in machine learning and nature
• Knowledge of data analysis and modeling
• Experience in the field of AI is an advantage
• Team and communication skills
• Willingness to cooperate with local authorities and to travel to Rastatt regularly