ArticleFrontiers in plant science2023
Deep learning-empowered crop breeding: intelligent, efficient and promising.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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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.
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Who cites it
11 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.Plant communications · 2026Review
- Smart agriculture in Asia.Plant communications · 2025Review
- Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields.Genes · 2025Review
- Blockchain-Empowered H-CPS Architecture for Smart Agriculture.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops.Plants (Basel, Switzerland) · 2025Review
- Integrative Approaches to Soybean Resilience, Productivity, and Utility: A Review of Genomics, Computational Modeling, and Economic Viability.Plants (Basel, Switzerland) · 2025Review
- Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision.Plant methods · 2024Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- Developing new sugarcane varieties suitable for mechanized production in China: principles, strategies and prospects.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.
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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 Socratic graph.