Junior Machine Learning Engineer
Identifying Potential Areas for Urban Agriculture in Milan, Italy | Local Chapter Project• Implemented exploratory image analysis techniques like RGB Histogram analysis, Blurriness Detection, Total Energy, and Median Absolute Deviation using OpenCV and PIL to extract key features from Apple Leaf images, contributing to enhanced quality of the image dataset. • Analyzed tabular crop data using statistical techniques such as univariate, bivariate, and variance inflation factors to identify key features for optimal crop selection. • Utilized Python scripts to automate the webscraping process, extracting geospatial image data from Sentinel 2 via Google Earth Engine (GEE) and QGIS, resulting in the identification of 50 potential urban farming areas in Milan, Italy. • Led the coordinated collection of geospatial data from various online sources, resulting in a 25% increase in project efficiency and accuracy among a team of 100 global collaborators. • Developed and deployed automated Python scripts to extract crucial data points for geospatial analysis, resulting in a 15% increase in efficiency in predicting optimal urban agricultural areas in Milan. • Developing customized interactive dashboards using Looker Studio to visualize urban farming areas, crop selection data, and smart pest management techniques, leading to a 20% increase in efficiency in decision-making processes.
