Artificial intelligence is used in this research to predict the shelf life of strawberries. The prediction of shelf life is based on temperature measurements from the moment a package of strawberries is harvested till the moment this same package is bought by a customer in a local PLUS supermarket. The strawberries are harvested in the south of Spain, near Huelva and distributed to local PLUS Supermarkets near Rotterdam. After the packages with strawberries, including temperature loggers, arrive at the supermarket shelf, the packages are moved to a shelf life room for visual inspection. During this daily inspection, the actual shelf life of the strawberries is determined by a classified inspector. The combination of the actual shelf life and the temperature profile through the supply chain is used to train, validate and test different machine learning algorithms. The most reliable shelf life prediction algorithm is the Exponential Gaussian Process Regression Algorithm, with the smallest confidence interval and an average deviation of 14.1 \%. To conclude, the possible improvements in the supply chain based on shelf life prediction, like traceability, food date labeling and quality grading are evaluated.

In this thesis, a swarm is defined as: ”A swarm is a large number of homogenous, unsophisticated agents that interact locally among themselves and their environment, without any central control or management to yield a global behaviour to emerge.” The bigger perspective is to have the units within a swarm operate for a potentially infinite amount of time, thus making algorithms and energy use as effective as possible. The research is split up in different sections: sensors, communication and behaviour. The behaviour is split again: individual behaviour, anti collision behaviour and swarming behaviour. The research on the sensors and communication points out that the Bluetooth Low Energy (BLE) sensor is the most suitable for an autonomous Zebro Swarm. BLE can be used to transfer data as well as serve as a range sensor. As a communication device, BLE offers the ability to advertise data an has a range of 50 meters which is sufficient for application in a swarm. As a sensor, BLE offers fairly accurate measurements in distances below 1 meter. Due to the properties of radio signal strength and the always present radio frequency noise, the accuracy decreased with increasing distances. The individual behaviour aims at exploring as much area as possible, with the amount of energy available. The energy use therefore has to be minimised and the explored area maximised. Since little is known on the energy use, this is assumed constant. Maximising area, however, has some key elements. The first is to keep moving; standing still results in energy use without exploring new terrain. The second is to make only gentle turns; the rotational centre must be outside of the detection distance. When this centre lies inside of the detection distance, the inner turn will cover area for a longer period making the turn ineffective. The anti collision behaviour should prevent units from making physical contact. By using a contactless sensor an approaching collision can be detected. Changing the parameters of the movement of the units at the moment they enclose more than a certain value prevents the collision. The anti separation behaviour should prevent units from separating from eachother. This can be done by making units turn 180 degrees once the relative distance reaches a certain limit. The turning on a certain limit is a simple algorithm and thus requires little processing power. Apart from the anti collision behaviour and the anti separation behaviour, there is another approach to deal with these behaviours. Since the units are able to communicate with each other, they can also advertise their location to other units. There are several methods for a unit to acquire their location. These methods are divided in approximate and exact location estimation methods. The Trial and Error method is placed in the first category, the exact location estimation methods are: Time Based, Internal Reference, External Reference and Radar. The External Reference method is considered the most applicable of these methods since it is relatively accurate, simple and provides a direct location.