Project title: Smart Green Wall
Project duration: October 1, 2025 – June 30, 2029

Specific objective: Development and expansion of research and innovation capacities and the introduction of advanced technologie

Intervention area: Research and innovation processes, technology transfer and collaboration between companies, research centers and universities with a focus on low-carbon economy, resilience and adaptation to climate change.

Focus topic: Energy & Climate

Website: smartgreenwall.eu/en

The climate is changing, and the need for adaptation is becoming increasingly urgent. Many regions are facing more and more problems such as heat accumulation, drought and heavy rainfall. Solutions for improving the microclimate, such as vertical gardens, which enable a significant reduction in temperature, noise, CO2, N2 and particulate matter, are already on the market. So far, these systems have been associated with high supply and maintenance costs, which often leads to a decision against their use, despite the undeniably positive effects.

To change this, a university and six SMEs from Germany and the Netherlands have joined forces in the ‘Smart Green Wall’ project to develop long-lasting products and services (TRL4->7/8) for the automated supply and remote maintenance of vertical gardens. The aim is to reduce operating costs through these innovations and to promote the widespread use of ‘green walls’ through autonomous operation.

In this project, smart stands for AI- and sensor-based management systems that enable autonomous plant care and monitoring and control through integrated systems. The management systems focus on the efficient use of energy and water. For example, all grey and rainwater is filtered, stored and used to irrigate vertical gardens in a closed system, which also helps to mitigate the effects of heavy rainfall events. The built-in solar panels and battery systems ensure the system’s energy independence.

The implementation of 5G connectivity allows for high-speed just-in-time data transmission and opens up additional functionalities such as supporting traffic control, management and autonomous driving. The adaptation of plant communities and optimisation of substrates for different uses and climate zones expands the range of applications for vertical gardens.

Project title: SHAPE – Soil Health Check and Practices

Website: shape-soil.eu

Soils worldwide are severely threatened for various reasons. This also applies to our German-Dutch border region, where agriculture and horticulture are among the most important economic sectors, and the valuable soils are essential for successful farming. Nevertheless, our soils are also suffering, and it is estimated that around 60% to 70% of the soils in the entire EU are unhealthy. Furthermore, around one billion tons of soil are lost to erosion every year, and the remaining fertile topsoil disappears. The EU estimates the cost of soil degradation at over €50 billion per year.

This soil degradation naturally has consequences for agriculture, which, already facing the enormous challenge of supplying the population with high-quality and affordable food while remaining economically viable, is grappling with the effects of climate change and steadily rising production costs. Healthy and highly functional soils have the potential to be a key to successful adaptation to the impacts of climate change and to contribute to slowing its progression by storing large quantities of CO2. Securing soil quality and maintaining and promoting soil health requires adapting current farming practices, which necessitates comprehensive knowledge, innovative analytical methods, and cultivation recommendations.

This is precisely where the “SHAPE – Soil Health Check and Practices” project comes in: The consortium of nine SMEs from Germany and the Netherlands, along with Rhine-Waal University of Applied Sciences, is developing innovative measurement and analysis systems together with application partners. These systems are integrated into a novel monitoring system that allows farmers to assess the current health of their fields at any time. Using machine learning, artificial intelligence, and complex simulations, the data is documented and evaluated in an integrated soil management system. Recommendations for soil health are generated and output via an integrated Decision Support System (DSS). Implementing the recommended measures conserves natural resources and prevents environmental pollution. The developed products and systems help to reduce the consumption of natural resources and prevent soil degradation and pollution of the water cycle.

This protects the soil and the environment in the program area. By increasing CO₂ sequestration in the soil and recording its impact, the goal of transitioning to carbon-negative farming can be supported, and potential future monetization of this service can be achieved. Initial initiatives, such as those from Rabobank and Bayer, are already developing experimental concepts to transform farmers into climate-friendly farmers. Furthermore, disseminating the soil knowledge developed within the project will maximize its impact in the region.

This project is financially supported by the European Union and the Interreg partners within the framework of the Interreg Germany-Netherlands program.

Project title: Energy Storage Governance (ESG)

The European Union is aiming for climate neutrality by 2050 and a 55% reduction in greenhouse gas emissions by 2030. The national authorities in Germany and the Netherlands have set themselves even more ambitious climate targets and are aiming for carbon neutrality as early as the 2030s.

The state as an institution wants to set a good example in terms of carbon neutrality in both the Netherlands and Germany, which is why the procurement of e-vehicles is the top priority in terms of mobility. However, e-mobility requires a radical change in public infrastructure, especially in the area of energy supply. The resulting dependencies on permanent electricity availability and stable networks pose major new challenges for public structures. In this context, existing emergency infrastructure safeguards based on fossil fuels need to be rethought, especially for critical infrastructure such as police forces, fire brigades, ambulance services, hospitals, etc.

Based on this problem, the consortium of seven partners (three public organisations, one SME, three knowledge institutions) is developing cross-border governance for the joint use of a cross-border storage and charging system for critical infrastructure. At the same time, the requirements criteria for this system are being developed and the system will then be built by an external supplier. The fully functional system will then be jointly tested and used by the German and Dutch security authorities in the border area.

The cross-border use of the storage and charging system will provide relevant insights and planning documents for future joint use for KRITIS. The governance is an exemplary blueprint for the cross-border joint use of infrastructure

Project title: Predictive AI: harvests & prices

The partners want to collaborate on product/prototype development for computer vision-based solutions for the rapidly growing greenhouse sector to enable operational optimization. This will enable greenhouse owners to automate, via mobile cameras, the counting and measure of fruit and vegetables in the greenhouse, which are currently two of the most time-consuming and complicated tasks in greenhouses.

In extensive ‘client discovery’ with greenhouses in the Netherlands and Germany we established that the demand for automation is high. This can only be done via ‘Computer Vision’ / CV (in essence, artificial intelligence applied to the recognition of fruit and vegetables), which in turn needs a “mobile component” so that the greenhouse does not have to use too many cameras.

In addition, the project partners use computer vision to understand the rate of growth of fruit / vegetables in the greenhouse, thus enabling greenhouse owners to predict and even influence when they can harvest how much by adjusting relevant inputs.

This addresses important pain points in the greenhouse industry such as reducing the current significant loss of produce due to misalignment between the timing of harvest and sale; or enabling greenhouse contract to make more accurate commitments vis-a-vis buyers such as supermarket chains about when how the greenhouse will deliver how much produce.

The project is structured into the following 3 work packages (WP):

1. Firstly, the development of camera mobility solutions. These consist of two ‘autonomous’ mobility solutions, namely drones and robots; and two non-autonomous solutions, namely cell phone app camera-based imaging and small ‘towers’ of cameras that can be integraated in existing transport solutions in greenhouses.
2. Secondly, transferability of the computer vision work to various crops.
3. Thirdly, linking CV-based prediction with price prediction to enable greenhouses to predict and plan their future revenue.

Project title: SPECTORS
Project duration: September 1, 2016 – August 31, 2020

Website: spectors.eu

SPECTORS is a Dutch-German high-tech initiative to unlock the market potential of civilian drone technology through sensor innovations for remote sensing and remote monitoring.

The civilian market for the use of drones and related sensor technology in remote monitoring, remote sensing, film production, and agriculture is immense. The US portal “Business Insider” estimates the market potential for civilian drone technology alone at over 12 billion US dollars in the next 10 years. SPECTORS leverages this market potential to create value in the region.

By the successful conclusion in February 2021, the interdisciplinary project activities had resulted in a multitude of products (demonstrators) with their associated processes and services, based on innovative hardware and software. These are distributed and offered by the participating regional SMEs. The success of SPECTORS means, specifically:

• 23 new products in a total of 11 product groups, focusing on sensor and drone technology for remote sensing and monitoring
• An interdisciplinary and complementary consortium of 31 partners – including 20 SMEs.
• Comprehensive expertise from the network, from the initial idea to the commercialization of the various products
• 40 new jobs will be created during the project period, with an additional 50 expected in the following five years.
• Securing over 840 jobs in companies and research institutions
• Internal and external workshops and symposia for interested parties, producers and users

Project title: IOA – Internet of Agriculture
Project duration: July 1, 2019 – October 31, 2022

IoA – Internet of Agriculture: Wireless Solutions for Digital Farming is a project within the strategic initiative “Agrobusiness & Food.” It promotes innovation in SMEs through cooperative research and development, as well as strengthening long-term, cross-border networks of expertise, connections, and synergies between companies, research institutions, and development centers. This cross-border pooling of expertise enables effective value creation in the growing market for smart farming, IoT wireless technologies, and specialized sensors in border regions.

The central objectives pursued in the project concern:

• Better utilization of agricultural production – efficient farm management and more effective cultivation
• Optimization of high-quality and sustainable agrological concepts – improved documentation, resource, yield and harvest management
• Precise production through the application of high-quality environmental technologies – resource efficiency, occupational safety and quality assurance in the production of agricultural goods
• Promoting resource efficiency – saving water and raw materials
• Cross-over to the “High-tech Systems and Materials (HTSM)” sector – application of new technologies, promotion of digitalization processes in agriculture (“Farming 4.0”)

The increased innovative capacity in the border region, demonstrated by the new products, contributes to boosting competitiveness, securing existing jobs, and creating new ones. These new products open up additional sales markets, which are then quickly established by the region’s innovative companies. To achieve these goals, RheWaTech, the project’s lead partner, is utilizing the open-source system farmOS, which is being adapted to specific requirements, in consultation with the other project partners.

Other companies and institutions from the agricultural sector were invited as associated partners at an early stage of the project to exchange knowledge and experience, thus creating a sustainable network of expertise that extends beyond the project itself.

Project title: Gärtners Grüner Daumen – Growing Support for Greenhouse Production
Project duration: January 1, 2020 to October 31, 2022

Website: interregv.deutschland-nederland.eu

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.