ArticleNature communications2024
An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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Who cites it
12 citing papers in PubMed, 53 citations in OpenAlex.
- Digital twins for plant-microbe interactions: Gap finding and filling.Plant communications · 2026Article
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- The role of induced resistance in mitigating postharvest fungal diseases in fruits and vegetables.Journal of advanced research · 2026Review
- From phenomics to post-phenomics: Multidisciplinary integration driving autonomous agricultural systems.Plant communications · 2026Article
- A multimodal geospatial foundation model anticipates crop stress and yield failure across climates and species.Frontiers in plant science · 2026Article
- Advancing sustainable agricultural transformation through the synergy of automated experimental platforms and living labs.Nature communications · 2025Article
- Continuous-time digital twin with analog memristive neural ordinary differential equation solver.Science advances · 2025Article
- Adaptive production strategy in vertical farm digital twins with Q-learning algorithms.Scientific reports · 2025Article
- Digital twin-based applications in crop monitoring.Heliyon · 2025Review
- A 3D reconstruction platform for complex plants using OB-NeRF.Frontiers in plant science · 2025Article
- Remote and Proximal Sensors Data Fusion: Digital Twins in Irrigation Management Zoning.Sensors (Basel, Switzerland) · 2024Article
- An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A digital twin is a digital representation that closely resembles or replicates a real world object by combining interdisciplinary knowledge and advanced technologies. Digital twins have been applied to various fields, including to the agricultural field. Given big data and systematic data management, digital twins can be used for predicting future outcomes. In this study, we endeavor to create an agricultural digital twin using mandarins as a model crop. We employ an Open API to aggregate data from various sources across Jeju Island, covering an area of approximately 185,000 hectares. The collected data are visualized and analyzed at regional, inter-orchard, and intra-orchard scales. We observe that the intra-orchard analysis explains the variation of fruit quality substantially more than the inter-orchard analysis. Our data visualization and analysis, incorporating statistical models and machine learning algorithms, demonstrate the potential use of agricultural digital twins in the future, particularly in the context of micro-precision and individualized agriculture. This concept extends the current management practices based on data-driven decisions, and it offers a glimpse into the future of individualized agriculture by enabling customized treatment for plants, akin to personalized medicine for humans.
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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.