The combination of increasing global food demand with increased food security risks associated with climate change amid a decreasing number of skilled growers necessitates innovative solutions in green- house horticulture. Autonomous growing offers a solution based on greenhouse climate forecasting and (optimal) control. However, current theoretical models developed for greenhouse climate forecast- ing face limitations due to the in-depth physics knowledge required for their use and their dependence on intricate system parameters that are difficult to estimate. Conversely, machine learning models struggle with generalisation to unseen conditions and adherence to physical laws, leading to unrealis- tic predictions in greenhouse environments. This study addresses these challenges by exploring the use of Physics-Informed Machine Learning (PIML) techniques to enhance greenhouse climate fore- casting. A simple differentiable theoretical model for simulating the greenhouse climate is proposed to serve as prior physical knowledge of the system. Subsequently, a novel PIML model is introduced in the form of Controlled Aphynity (CA), which integrates insights from neural controlled differential equations and Aphynity, and is the first such model that allows for the augmentation of incomplete prior knowledge of a dynamical system with data-driven machine learning models while being adaptable to changing dynamics due to forces acting on the system. Experimental results show that CA is capable of successfully augmenting incorrect physics under changing dynamics on the task of humidity deficit prediction in the greenhouse. Furthermore, three ensemble methods combining CA with traditional ma- chine learning techniques are explored and demonstrate promising synergies. A detailed case study evaluates CA and the best-performing ensemble approach on the task of humidity deficit prediction under realistic greenhouse scenarios over a complete crop cycle. Both CA and the ensemble methods exhibit superior adherence to physical laws, lower data requirements, and improved performance on outliers compared to conventional machine learning methods. This research contributes to advancing greenhouse climate modelling, underscoring PIML’s potential in optimising the greenhouse climate.F

Almende B.V., a technologically innovative and research-oriented company, has been working on a new algorithm that optimizes routes for parcel delivery trucks. The algorithm contains novel features, like including the possible use of autonomous vehicles, that are at this moment in time not taken into account in existing route optimization algorithms and thus visualization applications. To this end and to get a more tangible overview of the algorithm’s behavior and performance, they requested to have a customized visualization tool developed. This report describes the process and results of developing such a tool. The tool is presented as a single-page application and has been partly depicted on the cover of this document. The goal of the project is to have a more clear overview of the routing algorithm’s capabilities, by showing its unique features on a map and displaying statistics on the side. In addition, comparing the algorithm to existing ones should provide added insights into the (expected) benefits of the new algorithm. The main purpose of the tool developed in this project is to show insight into the workings of the algorithm and to help with enhancing and developing the algorithm. An added side-bonus is that the tool can also be used to show the performance to various groups of interested parties.