Classifying Physiological States of Polytrichum Moss Based on Digital Images Using Machine Learning

Mosses are widespread vegetations on the ground layer level in boreal forests. They play important roles in productivity, soil hydroclimate regulation and nutrient cycling in the ecosystem. The Polytrichum mosses are desiccation-tolerant and have two physiological states: a hydrated state and a desiccated state. The physiological features and growth rates of mosses differ in different states. Monitoring the physiological states of Polytrichum moss using near-surface remote sensing will be helpful in predicting the growth of mosses and assessing the vegetation condition in boreal forests. The initiative of this project is to classify the physiological states of the mosses based on digital images of moss canopies. In this project, we took images of moss canopies in fields and used OpenCV library to extract attributes that quantify the color and the structure of mosses from images. We then compiled a dataset with extracted attributes and use Weka Machine Learning Library to find ideal machine learning algorithms to do classification. The results showed that kNN classification algorithm had the best performance among the tested algorithms. The trained kNN model was used to predict images in the mixed state in multiple scales. The predictions were mapped back to the original images to compare with manual classifications of canopy images. On average, 66.4% of the area was predicted correctly. The median of this number was 74.1%. Overall, this model could provide a reasonable prediction of the physiological state of moss in images.