Gärtners Grüner Daumen is a project to use data to ease farm managers and growers in their daily work.

Quality improvement and work relief

The production of ornamental plants and vegetables along the German-Dutch border is an important economic activity in regional horticulture. However, the industry is coming under increasing pressure from rising labour costs, a shortage of skilled workers, international competition and increasing quality demands from market participants and consumers. Business leaders should increasingly work as horticultural “managers”.

Data-driven decisions

The “Gardener’s Green Fingers” project aims to help relieve these farm managers in their daily work. Digitisation, with artificial intelligence or machine learning techniques in particular, can help smaller companies increase their competitiveness.
The core of this project is the development of a self-learning “Decision Support System (DSS)”. Here, activities during cultivation are recorded with sensors and automatically documented. Machine learning methods are used to make connections from these generated observations in order to then derive rules from them. These links are made between current and historical environmental metrics (climate, light, plant, soil) and decisions made in cultivation (fertilisation, irrigation, climate change, etc.).
In the training phase, an interaction of the system with the grower could look like this: ‘If similar conditions prevailed in the greenhouse and on the plant in the past, you chose alternative A in 80% of the cases, B in 15% and C in 5%. Do you want to choose A again? ‘(We know such proposals in similar systems e.g. from online shop Amazon). As a result, the DSS learns continuously and can automatically initiate the necessary actions after a certain time and only inform the grower. The system can also derive rules in the event of changed behaviour in production itself, for example if a new culture is set up.

The training phase

The system automatically seeks interaction with the grower in the form of proposals. The DSS continuously learns from all the data and can automatically initiate necessary actions after a certain time and only inform the grower. The system can also derive rules in the event of changed behaviour in production itself, such as when a new culture is set up.