Phenological visual rhythms: Compact representations for fine-grained plant species identification

We extract plant color information from images and correlate with leaf phenological changes.We use time series associated with plants for pattern analysis and knowledge extraction.We present a novel approach for capturing phenological patterns from time series.Our method encodes time series as a visual rhythm, which is characterized by image descriptors.Our method presents high accuracy and computational speed on identifying plant species. Plant phenology, the study of recurrent life cycles events and its relationship to climate, is a key discipline in climate change research. In this context, digital cameras have been effectively used to monitor leaf flushing and senescence on vegetations across the world. A primary condition for the phenological observation refers to the correct identification of plants by taking into account time series associated with their crowns in the digital images. In this paper, we present a novel approach for representing phenological patterns of plant species. The proposed method is based on encoding time series as a visual rhythm. Here, we focus on applications of our approach for plant species identification. In this scenario, visual rhythms are 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 individual plant species from its specific visual rhythm. Additionally, our representation is compact, making it suitable for long-term data series.