Gärtners Grüner Daumen


Project duration

01.01.2020 - 30.05.2022

Funded by

INTERREG V A Deutschland - Nederland

internal cooperation

very close cooperation with Prof. Dr. Rolf Becker

project website

Short description

Garender's Green thumb: In this project a system is build to assist gardeners. Out of observations under which condition garderners perfom what kind of action, the technical system will learn rules how experts of a specific gardener company behave, the philosophy behind their actions etc. This knowledge can be used to support unexperienced staff in that company during their daily routines

Our part

Our research group is responsible for

  • Dokumentation system of used water and chemicals
  • Rule Engine
  • Decision Support System, including learning of rules


Yookr BV

Futura Flower Canders & Germes GmbH

RheWaTech Rhein-Waal Institut für Technologie UG


Aris BV

Kwekerij de Grenspaal

Ingenieurbüro Gröger


To assist the gardeners

The production of ornamental plants and vegetables is a significant economic activity in regional horticulture along the German-Dutch border. Rising labor costs, a shortage of skilled workers, international competition and increasing quality requirements by marketers and consumers are putting increasing pressure on the industry. Farm managers must become more and more horticultural managers. The project “Gardener’s Green Thumb” wants to make contributions that lead to a noticeable improvement and relief in company practice. Digitization and, in particular, artificial intelligence or machine learning techniques can also help micro-enterprises increase their competitiveness. The core of the project is the development of a self-learning decision support system (DSS). For this purpose, horticultural activities are first recorded with sensors and automatically documented. Using machine-learning methods, correlations between the current and historical environmental measurements (climate, light, plant, soil) on the one hand and the decisions of the horticultural management (fertilization, irrigation, climate change, etc.) on the other hand, will be identified from the generated observations, to derive rules out of it.

For example, in the training phase, an interaction of the system with the cultural guide might be: “If similar greenhouse and plant conditions prevailed in the past, 80% of the time you have chosen alternative A, 15% B, and 5% C. Do you want to choose A again? “(Such suggestions we know in a similar way, for example, from the Amazon Online Shop). As a result, the DSS continuously learns and could, after a certain time, automatically initiate the necessary actions and only inform the horticultural manager and, by observation, derive rules for changing behavior in production, for example when a new crop is cultivated.

Impressions & Documents