ArticleFrontiers in plant science2021
Deep Learning Algorithms Correctly Classify
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed.
- Deep learning algorithms enable accurate identification of cassava varieties (Frontiers in plant science · 2026Article
- Deep learning decodes species-specific codon usage signatures in Brassica from coding sequences.Scientific reports · 2025Article
- Construction of deep learning-based disease detection model in plants.Scientific reports · 2023Article
- Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed.Sensors (Basel, Switzerland) · 2023Article
- Dried shiitake mushroom grade recognition using D-VGG network and machine vision.Frontiers in nutrition · 2023Article
- Prospects of microgreens as budding living functional food: Breeding and biofortification through OMICS and other approaches for nutritional security.Frontiers in genetics · 2023Review
- Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.Frontiers in plant science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156
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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.