EIP Agri fundation 2021 ENG

We have recently been the subject of project funding within the framework of the European innovation partnership "Productivity and Sustainability in Agriculture". The network, known for short as "EIP Agri", is specifically intended to provide new impetus through innovations for agriculture.The network consists of Piccoplant Mikrovermehrungen GmbH, InProSens UG and Baumschulberatungsring Weser-Ems e.V ..

The aim of the project partners is to develop an indoor farm module that is to be used for the cultivation of secondary plant substances with the help of optical measurement methods. Spectroscopic methods in the near or mid-infrared range enable non-destructive and direct plant analysis to be carried out in seconds. In contrast to conventional laboratory analyzes, the sensors integrated in the indoor farm module can determine the content of secondary substances directly on living plants without contact. The first area of ​​application for the technology is said to be the rock rose. The end of the project is August 15, 2023.


2 Vaccinium

Since February 5th, 2019 we have been the subject of project funding as part of the European innovation partnership "Productivity and Sustainability", together with SeedForward GmbH from Osnabrück, the engineering and mathematics department of the Bielefeld University of Applied Sciences, as well as in an advisory role with the berry fruit experimental station in Langförden in agriculture". The network, known for short as "EIP Agri", is specifically intended to provide new impetus through innovations for agriculture.


Within the network we form the Operational Group (OG) "Vaccinium". The group deals with the development and use of smart fertilizers and plant strengtheners for organic blueberry cultivation.

In the course of the ecological orientation of many households in Germany, the demand for certified organic berries has increased. However, cultivation with the exclusion of agrochemicals brings challenges:

On the one hand by pest and disease infestation, on the other hand by a lack of nitrogen sources - organic manure only contains these in the form of relatively unstable ammonium. If the soil pH is> 5, iron absorption is also inhibited, which leads to deficiency symptoms and reduced yields. The declining biodiversity, e.g. due to the death of bees, also ensures lower harvest yields.

Due to such problems, organic blueberry farms currently only harvest around 3-4 tons per hectare. The profitability of these relevant special crops is therefore called into question.

Our goal is to minimize economic risk factors in organic blueberry cultivation and to achieve high-yield cultivation - especially through the use of innovative bio-based fertilizers and plant strengtheners. Procedures and approaches will also be made available to companies in other economic sectors during and after the project period.

A special information website for the project is being planned and will be linked here soon.

The end of the project is August 15, 2021

3 ProMap

Plant Robot for Multidimensional Artificial Phenotyping for Plasma Enhanced Research

1. Problem or current situation and need for action:

Despite its high efficiency, no-tillage poses major challenges for modern agriculture. Problems that often occur in a cluster, such as bacterial and fungal infections, viral infections transmitted by various harmful insects, and any deficiency symptoms that may occur, can usually only be countered with little other than high-dose application of pesticides and fertilizers. A solution to these problems requires a holistic concept as well as the networking of different areas of competence.

2. Innovationsidee und Lösungsansatz:

Innovation idea and approach:

The aim of the project is to combine novel methods of robotics, a multisensory data fusion for phenotyping as well as plasma technology solutions for the highly automated use in the agricultural sector to form a new overall system. This system should enable the efficient use of conventional plant protection measures and nutrient additives much earlier than with traditional farming systems. On the other hand, the use of pesticides should be significantly reduced or avoided through the early application of alternative treatment methods, such as plasma-activated liquids or the early removal of diseased young plants. The experiments are carried out in automated growing beds for the crops sugar beet, chard and lilac. For this purpose, a cost-effective planting robot, which has been designed for partially automated sowing, irrigation and weed suppression, is expanded into a multi-sensory and fully automated system. Machine learning methods are used to develop a model from a suitable amount of training data that is able to carry out classification tasks completely autonomously.

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