Deep Learning in Plant Phenological Research: A Systematic Literature Review

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016-2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field.

Automated summary

Climate change poses one of the most critical threats to biodiversity, affecting species interactions, ecosystem functioning, and biotic community assembly. Plant phenology research has become increasingly important due to the strong impact of seasonal and interannual climate variation on the timing of plant events. The feasibility of phenological monitoring relies on developing tools capable of efficiently analyzing large amounts of data. Deep Neural Networks, known for their impressive accuracy in learning representations from data, have shown significant breakthroughs in fields like image processing. This article presents the first systematic literature review of deep learning approaches in plant phenology research, analyzing 24 peer-reviewed studies published from 2016 to 2021. The methods applied in these studies are categorized based on phenological stages, vegetation types, spatial scales, data acquisition, and deep learning methods. The review identifies research trends and promising future directions, providing a systematic overview for this emerging research field.