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Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data
problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.
Compliance of agricultural AI systems – app-based legal verification throughout the development
(2024)
Significant advances in artificial intelligence (AI) have been achieved; however, practical implementation in agriculture remains limited. Compliance with emerging regulations, such as the EU AI Act and GDPR, is now vital, even for non-critical AI systems. Developers need tools to assess legal compliance, which is complex, often requiring full legal advice. To address this issue, we are developing a support app that simplifies the legal aspects of AI system development, covering the entire lifecycle, from conception to distribution. The current app, which covers the key legal area of copyright and will soon include GDPR and the AI Act, aims to bridge the gap between AI research and agriculture. An evaluation of our app by experts from both the legal and the IT domains shows that the app assists the developers so that they make legally correct statements. Consequently, it promotes legal compliance and awareness among developers, contributing to the seamless integration of AI into agriculture. The need for compliant AI systems in various industries, including agriculture, will only increase as regulations evolve.
The Internet of Things (IoT) is the enabler for new innovations in several domains. It allows the connection of digital services with physical entities in the real world. These entities are devices of different categories and sizes range from large machinery to tiny sensors. In the latter case, devices are typically characterized by limited resources in terms of computational power, available memory and sometimes limited power supply. As a consequence, the use of security algorithms requires of them to work within the limited resources. This means to find a suitable implementation and configuration for a security algorithm, that performs properly on the device, which may become a challenging task. On the other side, there is the desire to protect valuable assets as strong as possible. Usually, security goals are recorded in security policies, but they do not consider resource availability on the involved device and its power consumption while executing security algorithms. This paper presents an IoT security configuration tool that helps the designer of an IoT environment to experiment with the trade-off between maximizing security and extending the lifetime of a resource constrained IoT device. The tool is controlled with high-level description of security goals in the form of policies. It allows the designer to validate various (security) configurations for a single IoT device up to a large sensor network.
Bamboo is an environmentally friendly alternative to conventional materials in mechanical engineering such as steel or aluminium. Bamboo is the fastest growing plant in the world. Instead of releasing CO2 during the manufacturing process, bamboo absorbs CO2 as it grows.
In addition to the sustainability aspect, bamboo tubes also offer excellent properties as a lightweight construction material, which have been optimised through evolution. Bamboo tubes have high strength and stiffness at low weight when used as tension-compression bars or bending beams. Bamboo has strong, high-density fibres at the boundary area, where bending stresses are greatest. Towards the inside, where the stresses are lower, the bamboo becomes porous to optimise weight. This, together with knots arranged in regular intervals, counteracts buckling.
In mobile applications such as cars and bicycles, lightweight construction is sought for energy efficiency reasons. Because of its excellent lightweight properties, the project investigated whether bamboo could be used in mobile, automotive or agricultural engineering. For example, a bamboo bicycle frame has been developed with the aim to be as light as possible. There are bamboo bicycles on the market, but they can only be made one at a time by hand. The bamboo tubes are joined together and functional elements such as the bottom bracket and headset are integrated by wrapping them in resin-impregnated natural or carbon fibres. This makes the joints very heavy. A different approach is taken here: the bamboo tubes are drilled out slightly to achieve a defined internal diameter, and then short aluminium tubes are glued into the bamboo canes from the inside. To prevent the cane from breaking in the circumferential direction, i.e. perpendicular to the fibre direction, the bamboo tubes are wrapped in a thin layer of natural or carbon fibre impregnated with synthetic resin. The aluminium tubes and functional elements are welded or soldered together beforehand.
The design of the bicycle frame, i.e. the dimensioning of the bamboo tubes and joints, was based on extensive bending and tensile tests to determine the strength properties of the natural material bamboo. The bonding between the bamboo cane and the aluminium tube was also investigated experimentally. Finally, several prototype bicycle frames were made and tested for durability according to DIN-EN-14764. The frames passed the tests.
The result is a bamboo bicycle that is manufactured with standardised connectors and joints. The assembly concept developed allows both fully automated and semi-automated series production of bamboo bicycles.
The energy transition involves various challenges. One key aspect is the decentralization of power generation, which requires new actors. In order to integrate these into the system in the best possible way, there are various approaches e.g. in cooperation in citizens' initiatives or cooperatives (Dorniok, 2016).
Cooperation in general can enable the implementation of certain business models or can increase profitability by the exploitation of economies of scale (Skovsgaard & Jacobsen, 2017; Theurl, 2010). Synergy effects result from the utilization of know-how, different technologies or resources of the partners involved to complement the own competencies and services (Eggers & Engelbrecht, 2005; Sander, 2009). Cooperation exists in various industries and enable the participating companies to compensate their size-related resource deficits (Glaister & Buckley, 1996; Todeva & Knoke, 2005). This creates the opportunity to develop innovations, open up new markets, exploit newly created economies of scale and share costs and risks (Franco & Haase, 2015). In agriculture, cooperation in the form of cooperatives have been of essential importance for a long time, especially with the aim of exploiting synergy effects (Bareille et al., 2017). In the field of renewable energy development, cooperation in form of citizen cooperatives make a significant contribution to the participation of citizens in political, social and financial aspects of the energy transition (Huybrechts & Mertens, 2014). Energy cooperatives are frequently discussed as a potential actor in the energy transition and are increasingly being established to advance the common interests of stakeholders. For example, the joint operation of decentralized power generation plants can involve new actors in the energy transition through regional cooperation (Walk, 2014).
Existing biogas plants in Germany need new business models after the 20-year Renewable Energy Sources Act feed-in tariff expires. For continued operation, a business model innovation is needed, which can be realized based on the different technical utilization pathways. Cooperation can have a significant impact on the profitability of the different business models, especially by exploiting synergy effects (Karlsson et al., 2019). In addition, cooperation can help to ensure that existing plants continue to operate at all.
Currently, the most widespread use of biogas in Germany is in the coupled generation of electricity and heat. Additionally, there is the possibility of upgrading biogas to biomethane or biogenic hydrogen path (Mertins & Wawer, 2022).
Different options for cooperative business models that exist in the biogas utilization pathways are presented. The focus is on explaining the advantages of a joint approach compared to single-farm business models and identifying the relevant actors. Subsequently, drivers and barriers for the different cooperative business models are identified and classified based on 20 semi-structured interviews with plant operators in the administrative district of Osnabrück. The aim is to identify drivers and barriers for cooperative post-EEG operation. As a result, political instruments are to be found that make it possible to involve relevant actors and thus stimulate the best possible continued operation from the point of view of the energy system. The results are structured according to the PESTEL analysis. This assigns drivers and barriers to the categories political, economic, sociocultural, technological, ecological and legal (Kaufmann, 2021). The analysis of the interviews is supplemented and validated by a literature review.
Drivers and barriers for cooperative business models are manifold and can vary mainly depending on the plant and the operator.
Drivers
• Political
o Promotion of renewable energies: reduce dependence on fossil (Russian) fuels
• Economic
o Expectation of synergies (information sharing, shared risk, economies of scale)
o Planning security (fixed supply or purchase contracts)
o Access to new markets (not accessible by single-farm business models)
o Cost savings by sharing infrastructure, technology
o Positive return expectation
• Sociocultural
o Motivating, innovative environment
o Lowers barriers to participation in new markets
o Target-oriented partnerships
o Better use of capacities and strengths
o Strengthening regional value creation
• Technological
o Economies of scale (efficiency)
o Available, mature technology
o Storable, transportable gas
o Well-developed infrastructure
• Ecological
o Increase in plant efficiency
o Reduction of greenhouse gas emissions
o Promotion of the circular economy by utilization of organic waste and agricultural residues
o Improving soil quality (fermentation residues as fertilizer)
Barriers
• Political
o Competition to other renewable energies
• Economic
o Uncertainty about future development of energy markets
o Disagreements between the cooperation partners
o Lack of flexibility due to longer-term contractual obligations
o Allocation of profits
• Sociocultural
o Cooperation with current competitor
o Cultural differences and lack of trust
o Acceptance by the general public (e.g. overproduction of maize)
• Technological
o Different technology that is difficult to combine
o Data protection
• Ecological
o Competition for agricultural land
o Use of monocultures
o Emissions from plant
o Pollution from transport
• Legal
o Legal requirements and regulations
o Unfavorable regulatory environment, e.g. long permitting process
One finding is that uncertainty is a major barrier for plant operators. This includes uncertainty about regulatory frameworks and political requirements, as well as about the general development of the energy markets. In addition, social factors such as lack of reliability and disagreement about revenue sharing are a potential barrier. A key driver for the implementation of cooperative business models is the expectation of synergy effects. In addition, operators are driven by a positive expectation of returns and the responsibility for securing the energy supply in times of crisis.
The drivers identified can now be used to develop strategies to advance cooperative business models. In particular, synergy effects should be exploited so that operators can benefit from cooperation. The advantages can also be highlighted and communicated to increase acceptance among the general public. Another important step is to reduce the barriers discussed above. In order to reduce social barriers in particular, it may be advisable to include an external partner in the cooperation, such as a municipal utility that operates an upgrading plant and concludes purchase agreements with the individual partners. In addition, it would be politically expedient to provide the operators with a clear framework for the future in order to reduce uncertainties. As a further aspect, knowledge transfer on new technologies and markets should take place.
Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is prevalent in all other steps of developing AI, from data analysis via data selection and cleaning to performance evaluation. During AI architecture design, HMI can immediately highlight unproductive layers of the architecture so that lightweight network architecture for embedded applications can be created easily. We show that by using this HMI, users can instantly distinguish which AI architecture should be trained and evaluated first since a high accuracy on the task could be expected. This approach reduces the resources needed for AI development by avoiding training and evaluating AI architectures with unproductive layers and leads to lightweight AI architectures. These resulting lightweight AI architectures will enable HMI while running the AI on an edge device. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths. In our AI-in-the-loop approach, the AI remains the working horse and primarily solves the task. If the AI is unsure whether its inference solves the task correctly, it asks the user to use an appropriate HMI. Consequently, AI will become available in many applications soon since HMI will make AI more reliable and explainable.
Currently, soil nutrient analysis involves two separate processes for soil sampling and nutrient analysis: 1. field soil sampling and 2. laboratory analysis. These two - separate - main work processes are combined and conceptualised for a mobile field laboratory so that soil sampling and analysis can be carried out simultaneously in the field. The module-based field laboratory "soil2data" can carry out these two main work processes in parallel and consists of 5 different task-specific modules that build on each other: app2field, field2soil, app2liquid, liquid2data and data2app. The individual modules were designed and built for the sub-process steps and adapted to the special features of the mobile field laboratory "soil2data". The biggest advantage is that the analysis results are available immediately, and a fertiliser recommendation can be generated instantly. For further analyses, the results are stored in the data cloud. The soil material remains in the field. In the ongoing project "Prototypes4soil2data", the mobile field laboratory soil2data is being further developed into a prototype with a modular structure.
Knowledge of the small-scale nutrient status of a field is an important basis for decision-making when it comes to optimising the fertiliser use in crop production. Currently, the traditional method involves soil sampling in the field and soil sample analysis in the laboratory as two separate working processes.
The previous research project "soil2data" developed a mobile field laboratory for different carrier vehicles. In the follow-up project "prototypes4soil2data", the results of soil2data are further developed. A mixed soil sample is collected during the drive on the field. The soil sample is then wet-chemically prepared and analysed. The overall soil sampling and analysis process is divided into the following process steps: soil sampling planning, soil sampling, soil preparation, soil analysis and data management. The process steps are modified for the mobile field laboratory and the process steps run in parallel. The new soil extraction method is based on official German methods (VDLUFA) to ensure the interoperability of the analysis results with the VDLUFA fertiliser recommendations. An innovative key component is the NUTRISTAT analysis module (lab-on-chip with ISFET measurement technology). It can measure pH, the nutrients NO3-, H2PO4-, K+ and the electrical conductivity. In addition to the advantages of rapid data availability and no need to transport soil material to the laboratory, it provides a future basis for new application, e.g. verification of current results in the field during soil sampling with existing results or dynamic adjustment of soil sampling during work in the field.
The expiry of national subsidies for biogas in Germany means that new business models are needed. Furthermore, hydrogen is expected to make a significant contribution to the energy transition in the future. Therefore, potentials for the production of hydrogen from biogas are identified in this study. A joint upgrading infrastructure is developed that models the collaborative upgrading of biogas to hydrogen for existing biogas plants with subsequent gas grid injection. Furthermore, regions are identified that are particularly suitable as pioneer regions in Germany due to a high potential for green hydrogen production and comparatively low costs for hydrogen production. The modeling shows that collaborative upgrading achieves significant cost savings compared to single-farm upgrading. Furthermore, the potential for hydrogen production from biogas and the costs of upgrading differ significantly within the administrative districts in Germany.