AI-Based Approaches for Document Processing, Forecasting, Supply Chain, and Precision Indoor Agriculture
Partly accelerated by the COVID-19 pandemic, AI-Based Approaches for Document Processing, Forecasting, Supply Chain, and Precision Indoor Agriculture are receiving increasing attention in terms of innovation and broad deployment. Research can be conducted during summer, possibly beginning immediately, in the following areas:
AI-based Approaches for Precision Indoor Agriculture: Due to shrinking farmlands and shortage of resources, various nations are now facing the challenge of feeding their rapidly growing population nutritious and safe food. With plant-focused light sources and crop-specific lighting recipes, the intent is to enable farmers to adopt Precision Agriculture techniques which allows them to grow crops up to 20x faster and gain up to 20x higher yield in a more sustainable way. Responsive lighting system that adapts its recipe based on plants and farmers’ needs will be the next big thing in Agricultural lighting. The intent is to conduct research and to design, develop, train, test, and validate new AI-based models and algorithms to predict (a) harvesting time and (b) yield of specific plants in controlled environments such as greenhouses and indoor farms under the influence of artificial lighting. The approach is based on multi-model data fusion and machine learning algorithms that employ information from image sensors, environmental sensors, weather stations, historical yields, crop data and other farm data. The main focus will be on tomato and medicinal cannabis indoor crops.
AI-based Demand Forecasting and Supply Chain Management in COVID-19 era: The coronavirus pandemic has led to an urgent need to analyze data and information collected with automated and semi-automated processes and integrate them with existing computer-based information to create and test forecasting models and predictive methods related to the price, lead time, and stock of different critical items on near-term and continuing basis.
Document Processing with AI & OCR and Neural Network Application in Financial and Medical Realms: Previous research by MIT EECS students led to a broad patent on automated reading and processing of bank checks, a concept now used in many countries. This project aims to develop techniques to reduce the human effort involved in transferring information from diverse media to computers, including ID cards, 1099 and W2 forms, and diverse financial and medical documents. This involves: the detection of the boxes containing the desired information and fields within the box; the separation of material that is in printed and typewritten format versus material in handwritten format so that they can be processed independently with greater automation employed for processing of printed and typewritten material; the classification of the document into one of several categories, each with its own recipe in terms of further processing; and the examination of the document with neural network with respect to potential fraud, security, or other concerns. Artificial Intelligence and Contextual Knowledge will be used to find relevant boxes of information, make preliminary assessment of what the contents of the boxes are, integrate and reconcile the new information with legacy information on computers, and then provide the integrated information for use by financial and medical professionals.
Pre-requisites: Prior experience in AI, lighting, supply chain, forecasting or agriculture that can be leveraged for this research endeavor. Please send resume, project of interest, and indicate preference for pay/credit/either. Preference will be given to rising seniors and juniors.