Color based classification of circular markers for the identification of experimental units

Abstract

The purpose of this project is to analyze the growth of plants under certain lighting conditions. In order to ensure ideal lighting for all plants under demanding conditions like lack of optimal light due to shadowing, side wall reflections, overlapping of plants, etc., pots are rotated manually in an irregular fashion. To keep track of the position of these plants from time to time, a marking system is used for each tray of 16 plants. These markers are unique for each tray High definition surveillance cameras placed above these plants capture the plant images periodically. These images undergo image processing. Image processing should be able to identify and recognize the plants from the identification markers that were placed within each tray and thereby draw the statistics about the growth of the plants. Hence the computing part of this project is all about extracting the identity of a plant through image processing. Image processing involves object and color recognition. Fiji, an image processing tool, is used for object recognition and the Python image module called “Image” is used for color recognition. Object recognition accurately locates the position of these circular objects and measures their size and shape. Color recognition identifies the pixel values of these circular objects. Finally the code corresponding to three-element groups of these circular units is fetched and stored. This code gives the identity of the tray and, therefore, each plant. The timestamp that is stored with each plant image along with the code fetched through image processing is used to track the location of a plant in the plant chamber through time.