Project 1 (Year 2022): Minimizing cost of electricity for agricultural farm through optimal operation of renewable energy system

 

Agricultural operations are vulnerable to climate change. The installation of onsite renewable energy offers agricultural operations an opportunity to reduce greenhouse gas (GHG) emissions, increase energy reliability, and realize the benefits associated with reduced demand for grid electricity.

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Project 2 (Year 2024): Identifying the diseases of apple orchards based on the images of the leaves

 

Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts. In recent years, digital imaging and machine learning have shown great potential to speed up plant disease diagnosis. Computer vision methods are being developed to make use of digital images of symptoms for disease classification. These methods combine human expertise and machine learning algorithms to find relationships and visual patterns for grouping and identification. In recent years, many crowdsourced image platforms have become popular among data scientists to solve complex “big data” problems. This research project will use a specific dataset to identify apple orchard diseases using deep neural network.