Plant Species Identification with Phenological Visual Rhythms

Plant phenology studies recurrent plant life cycles events and is a key component of climate change research. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful are digital cameras, used as multi-channel imaging sensors to estimate color changes that are related to phenological events. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract individual plant color information and correlated with leaf phenological changes. To do so, time series associated with plant species were obtained, raising the need of using appropriate tools for mining patterns of interest. In this paper, we present a novel approach for representing phenological patterns of plant species derived from digital images. The proposed method is based on encoding time series as a visual rhythm, which is characterized by image description algorithms. A comparative analysis of different descriptors is conducted and discussed. Experimental results show that our approach presents high accuracy on identifying plant species.