Main Topics
My research primarily involves looking into machine learning and intelligence based systems. This ranges from proposing possible methods for improving a solution to optimizing existing systems. Priumarily, my interests lie in improving renewable energies and food-based systems for a better future. I believe machine learning and data analysis can be utilized to its fullest potential to benefit not only people, but the planet itself. We must respect our planet to ensure healthier crops and better general weather.
Automated Detection of Harmful Substances in Crops
For a computing topics research team, I performed group-based survey research on food safety found in the food supply chain. As this is a topic that has little research focusing on the whole
supply chain and how it relates, my team wished to fill in gaps and find relations while proposing solutions to improve safety. This is important as with globalization increasing and networks becoming
more interconnected, the importance of keeping food safe and minimizing the spread of disease is highlighted. My main research task was focusing on the beginning of the supply chain, agriculture.
Considering agriculture, it can be broken into both crops and livestock. For crops, the three main areas of concern are soil, water, and the crops themselves. I proposed a methodology of utilizing
soil and water sensors alongside cameras to directly monitor farms for harmful substances such as: foodborne pathogens, pesticides, and toxins. The concept is training machine learning models based on different
regional data in combination with ensemble-based stacking to improve generalization and standardization for a model. If these models can be trained on different regional data and pick up on the nuances of different farms,
it will help with the detection of these harmful substances. At the same time, detection for soil, water, and crop based substances will be independent from one another, allowing for a more
flexible implementation.
Optimizing the Placement of Components in Hybrid Wind-Solar Farms
My first research-based assignment, the topic was as broad as focus on an optimization problem. As my interests lie in sustainability and the environment, I looked into different systems for improving renewable energies. This lead me to renewable hybrid wind-solar farms which can have a variety of configurations. The main idea is to increase energy production while reducing costs. This involves where to place hybrid and solar wind components based on the geography and weather of the region. There is an installation fee as well as maintenance fee involved with each. The methodology I looked into involves using particle swarm optimization algorithms, genetic algorithms, and cuckoo search algorithms. I found that the cuckoo search algorithm performed the best and was the most robust in approach. To further look into the problem, using dynamic yet robust algorithms and include more regional based data could see improvements. It is a challenging concept as each region has different amounts of wind and sunlight.