One of the main problems with Instance-Level Image Retrieval in video data is that for query videos with multiple objects of the same instance, extracting features from keyframes of this query video is time consuming. This thesis aims to solve this problem by implementing a Convolutional Neural Network based approach, which significantly reduces the extraction time and increases the accuracy. After analysing multiple methods, Second-Order Loss and Attention for image Retrieval (SOLAR) was found to be the most promising method. SOLAR will be tested based on three performance metrics: the mean average precision, the recall and the extraction time per image. The performance will be evaluated based on a selection of videos and images from a dataset provided by Dr. Andrea Nanetti from the Engineering Historical Memory project. For this dataset SOLAR achieved a mean average precision of 83 %, a recall of 85 % and an extraction time of 0.73 seconds per image. To conclude, SOLAR is specialised in detecting and describing landmarks due to its pre-trained model, but for a more general case the backbone model should be trained differently which will increase the accuracy. Future work could also include speed improvement by looking at object detection methods.

Many countries in the world face the problem of fruit frost during spring, which results in the loss of harvested crops. Solutions exist to prevent the fruit to freeze, however this requires that the temperature is known. Therefore, a smart autonomous temperature sensor network is designed that is capable of acquiring a 3D temperature profile of a fruit orchard to detect fruit frost. This thesis discusses the design of the energy harvesting and control module, which is one of the three sub-modules of the system. The goal is to sustain the energy demands of the sensor in a durable way, in order to operate for 20 years without requiring maintenance. To achieve this, several ambient energy sources and their harvesting techniques are investigated and simulated to examine the energy that can be harvested. This resulted in the use of solar energy, harvested by a solar cell and controlled by a maximum power point tracking module. Next, different energy storage implementations are considered to store the harvested energy temporarily. The use of a 5 F supercapacitor is concluded. Furthermore, an energy monitoring circuit is designed to measure the energy stored in the supercapacitor. Full system reliability simulations are done to verify the complete design by utilising the solar irradiance and temperature data of the past ten years. From these simulations is concluded that the design can sustain the energy demands of the sensor at all times.

In this thesis, the design and implementation of a wireless communication module and an accompanying network are discussed. This wireless communication module is used in a device which communicates frost temperatures measured at orchards. The goal is to gather data on how fruit frost develops on orchards, and to warn a fruit farmer if it starts freezing. First, several wireless communication protocols are discussed and the one best suited for the application is chosen. It is decided to use the LoRaWAN communication protocol. Then, the off-the-shelf hardware components which are required to implement the LoRaWAN communication protocol are chosen. Furthermore, LoRa communication parameters and the LoRaWAN network structure are discussed. A scheduling system is designed and proposed to increase the reliability of the network. At last, network simulations are performed to verify the chosen implementation.

With increasingly changing climates, measuring the weather on remote, off-grid locations has become more important in order to accurately track emerging weather patterns. Cheap and easy access to meteorological information allows communities and individuals to better manage agricultural development, track localised extreme weather events, and expand knowledge of ground-true weather conditions for remote or hard to access locations. Nowadays, with the growth of modern technology and the rise of the Internet-of-Things, it has become possible to design a long range, low maintenance, affordable, continuously operating autonomous wireless weather station powered off-grid by renewable resources. This thesis explores the feasibility of such a self-supporting wireless station, and attempts to construct a prototype to support the findings. A multitude of solutions are discussed to tackle the different subsystems and their functions, culminating in a proposal for a weather station design. Low maintenance sensors are used to acquire weather data and LoRaWAN is used to send that data over long ranges with a low power draw. The station uses solar energy and li-ion batteries to sustain the system, which are scaled to prevent loss of power. A simple microcontroller unit manages the power and data flows of the system. Long term simulations of the design show the proposed system would be able to operate continuously throughout the year, while measuring every minute. The report also discusses the apparent strengths and shortcomings of the system, and suggests potential improvements that could help the system achieve a better long-term performance.

Fruit-frost during spring is one of the main causes of damage to the harvest of fruit in orchards. Various systems using different methods of preventing spring-frost are available on the market. To determine when these systems should be activated, the temperature in the orchard needs to be determined. In this project, a self-sustaining autonomous temperature sensor network is designed, which is capable of making a 3D temperature map of the orchard. The system is used to warn farmers when the threat of fruit-frost occurs and to gather data on the spatial variation of the temperature in the orchard. In this thesis, the focus is put on designing the smart measurement and control system. This includes choosing an appropriate control unit and temperature sensors. Also, the software for the control unit is designed, which allows for a smart measurement scheme that balances energy usage and measurement frequency. Finally, an estimation of the energy usage of the subsystem is given based on theoretical analysis.