OOIR: Observatory of International Research

Papers

(The H4-Index of Applications in Plant Sciences is 22. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)

Article	Citations
The Plant Pathology Challenge 2020 data set to classify foliar disease of apples	99
Machine learning: A powerful tool for gene function prediction in plants	66
Strategies for reducing per‐sample costs in target capture sequencing for phylogenomics and population genomics in plants	65
Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots	64
WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data	50
A target enrichment probe set for resolving the flagellate land plant tree of life	43
LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens	37
New targets acquired: Improving locus recovery from the Angiosperms353 probe set	36
High‐throughput methods for efficiently building massive phylogenies from natural history collections	36
Field‐based mechanical phenotyping of cereal crops to assess lodging resistance	33
Plants meet machines: Prospects in machine learning for plant biology	32
A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction	28
A comparison of seed germination coefficients using functional regression	27
A two‐tier bioinformatic pipeline to develop probes for target capture of nuclear loci with applications in Melastomataceae	26
Inferring the impacts of evolutionary history and ecological constraints on spore size and shape in the ferns	25
On the potential of Angiosperms353 for population genomic studies	25
Botanical microbiomes on the cheap: Inexpensive molecular fingerprinting methods to study plant‐associated communities of bacteria and fungi	25
HybPhaser: A workflow for the detection and phasing of hybrids in target capture data sets	25
Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning	25
Digitally deconstructing leaves in 3D using X‐ray microcomputed tomography and machine learning	23
The best of both worlds: Combining lineage‐specific and universal bait sets in target‐enrichment hybridization reactions	22
A basic ddRADseq two‐enzyme protocol performs well with herbarium and silica‐dried tissues across four genera	22