ReviewSensors (Basel, Switzerland)2023
Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Coformer: a deep learning-based framework for cross-environment and multi-year cotton phenotype prediction and interpretation.Plant methods · 2026Article
- A unified RepVGG model factory with plug-in SE attention and GeM pooling for fine-grained agricultural classification.Scientific reports · 2026Article
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- Integrated Moldboard Ploughing and Organic-Inorganic Fertilization Enhances Maize Yield and Soil Fertility in a Semi-Arid Region of North China.Plants (Basel, Switzerland) · 2025Article
- Smarter stomata: emergent technologies unlocking yield potential in a changing climate.AoB PLANTS · 2025Review
- Digital twin-based applications in crop monitoring.Heliyon · 2025Review
- ASAS-NANP Symposium: mathematical modeling in animal nutrition: harnessing real-time data and digital twins for precision livestock farming.Journal of animal science · 2025Review
- Cloud-edge-device collaborative computing in smart agriculture: architectures, applications, and future perspectives.Frontiers in plant science · 2025Review
- A 3D reconstruction platform for complex plants using OB-NeRF.Frontiers in plant science · 2025Article
- Protective Effects of Peanut Sprouts from a Smart Farming System on the Barrier Function of Human Epithelial Cells.Preventive nutrition and food science · 2024Article
- Innovative food supply chain through spatial computing technologies: A review.Comprehensive reviews in food science and food safety · 2024Review
- Beehive Smart Detector Device for the Detection of Critical Conditions That Utilize Edge Device Computations and Deep Learning Inferences.Sensors (Basel, Switzerland) · 2024Article
- Digital Twins in Agriculture: Orchestration and Applications.Journal of agricultural and food chemistry · 2024Review
- Digital Twins in Agriculture and Forestry: A Review.Sensors (Basel, Switzerland) · 2024Review
- An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture.Nature communications · 2024Article
- The Future of Food.Foods (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.
Indexed as
Identifiers
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.