Deep Learning in Plant Phenological Research: A Systematic Literature Review

Abstract

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.

Plant phenotyping relevance

植物のフェノロジーを対象とする深層学習手法を体系的にレビューし、データ取得法と解析法を分類しており、方法論レビューが中心です。

Code and data availability

This is a systematic literature review of deep learning in plant phenology. The authors report no phenotype datasets, images, code, or models of their own; the data availability statement only points to the article/supplementary material (the review itself), and all datasets/models mentioned belong to cited prior work.

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