The increasing global food demand, accompanied by the decreasing number of expert growers, brings the need for more sustainable and efficient solutions in horticulture. Consultancy company Delphy aims to face this challenge by taking a more data-driven approach, by means of autonomous growing inside the greenhouse. The controlled environment of greenhouses enable data collection and precise control. Delphy's current solutions focus on the needs of the crop, but a grower also needs to consider the economic aspect of taking certain decisions on the greenhouse climate. A potential method for solving this complex problem is Constraint Programming (CP). In this work, the applicability of CP for the greenhouse economic optimal control problem will be studied. The contributions of this work are threefold; First, the greenhouse climate is modelled with Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) machine learning models. Secondly, an LSTM model is embedded into a CP model. Lastly, the profit of the grower is optimised through this CP decision support system (DSS). The performed experiments show that both types of time-based machine learning models can model greenhouse temperature and humidity deficit with reasonable accuracy, while light and CO₂ are harder to predict. The correctness of the LSTM-in-CP embedding is validated. The implemented DSS is not yet practically applicable, as the search space is too large to come to reasonable results for realistic instances. For small instances however, the DSS is able to improve the decisions of the grower, demonstrating the potential of using CP for economic greenhouse decision making.

Fraudulent behavior within online marketplaces is a prominent but unsolved problem. Most marketplace operators try to mitigate this behavior by serving as the central authority. This approach requires user data collection and is not privacy-friendly. In an attempt to build a foundation for solving the fraud concerns and privacy issues, this paper elaborates on the design and implementation of a simple marketplace system using peer-to-peer (P2P) technology in combination with a Self-Sovereign Identity (SSI) solution. The P2P network ensures no single points of control, reduces risks of a big data breach and simply costs less to operate. The SSI solution makes sure that users cannot create multiple accounts to whitewash their dishonest behavior. Ensuring every user has only one identity makes the platform Sybil-resistant. In contrast to other identity verification systems used in marketplaces, such as Facebook Login, SSI aims to put the user in control and to not collect personal data. Users know what data are asked and give explicit consent for each request. This user-centric approach makes them privacy-friendly. Reaching Sybil resistance without having a central authority in a marketplace has not been done before. In the future a reputation system can be built on top of the Sybil-resistant P2P system, ensuring users’ behavior can not be whitewashed. Several methods are used during the design and the implementation process. They include the Scrum framework, MoSCoW prioritization and Class-Responsibility-Collaboration cards. Git was used for version control while code quality was kept high through a custom CI setup. Additionaly, every merge request required at least two approvals to ensure thorough code review. This resulted in an application that is both Sybil-resistant and privacy-friendly