SynthesisFrontiers in plant science2022
Deep Learning in Plant Phenological Research: A Systematic Literature Review.
Synthesis in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 76 citations in OpenAlex.
- Phenotypic Differentiation Between Highland and Coastal Quinoa Under Cold Stress Conditions.Plant, cell & environment · 2026Article
- Phenological responses to temperature are similar across spatial and temporal gradients - lessons learned from European long-term data.International journal of biometeorology · 2026Article
- Multi-scale spatial-temporal remote sensing fusion for phenology identification in rice germplasm resources.Plant phenomics (Washington, D.C.) · 2026Article
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Cold hardiness dynamics predict budbreak and associated low-temperature threats in grapevine.The New phytologist · 2026Article
- Rapid Surveys Reveal Temporal Variation in Flowering Community Phenology in a Great Basin Desert Ecosystem.Ecology and evolution · 2026Article
- Open RGB imaging workflow for morphological and morphometric analysis of fruits using deep learning: a case study on almonds.GigaScience · 2026Article
- Quantifying Phenology in the Deciduous Tree and Phytophagous Insect System: A Methodological Comparison.Ecology and evolution · 2025Article
- Expanding phenological insights: automated phenostage annotation with community science plant images.International journal of biometeorology · 2025Article
- Digital Repeat Photography Application for Flowering Stage Classification of Selected Woody Plants.Sensors (Basel, Switzerland) · 2025Article
- Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition.Frontiers in plant science · 2025Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- Multispecies deep learning using citizen science data produces more informative plant community models.Nature communications · 2024Article
- Enhancing Apple Cultivar Classification Using Multiview Images.Journal of imaging · 2024Article
- Opportunistic plant observations reveal spatial and temporal gradients in phenology.npj biodiversity · 2024Article
- Geometric Morphometric Versus Genomic Patterns in a Large Polyploid Plant Species Complex.Biology · 2023Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Bridging the gap: how to adopt opportunistic plant observations for phenology monitoring.Frontiers in plant science · 2023Article
- Crop-saving with AI: latest trends in deep learning techniques for plant pathology.Frontiers in plant science · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.