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AI-based Approaches for Precision Indoor Agriculture adsa




CSAIL: Computer Science and Artificial Intelligence Lab

Faculty Supervisor:

Amar Gupta

Faculty email:


Apply by:

09/01/2020; earlier preferred; selections made on rolling basis


Jessica Liu: jvliu@mit.edu

Project Description

With plant-focused light sources and crop-specific lighting recipes, farmers can 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 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 is on tomato crops.


Senior and juniors with some background AI and/or plant growth area preferred.