ArticleHeliyon2023
Application of digital technologies for ensuring agricultural productivity.
Article in Heliyon, 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
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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
16 citing papers in PubMed.
- Socioeconomic evaluation of feeding strategies and profitability in dual-purpose livestock systems settled in the southwest of the state of Mexico.Tropical animal health and production · 2026Article
- Dynamics of Agriculture 4.0 Technology Adoption in the Agri-Food System: Insights from an Exploratory Study in Rio Grande do Sul-Brazil.Foods (Basel, Switzerland) · 2026Article
- Cultivated Meat and the Future of Food Systems: Promise, Progress, and Challenges.Food science & nutrition · 2026Review
- Review
- Smart farming approaches in medicinal plant cultivation: a review of techniques, benefits, and sustainability.Planta · 2026Review
- Development of Real-Time Water-Level Monitoring System for Agriculture.Sensors (Basel, Switzerland) · 2025Article
- Smartphone-Integrated Electrochemical Devices for Contaminant Monitoring in Agriculture and Food: A Review.Biosensors · 2025Review
- Evaluation for the selection of agricultural spraying drones using p, q-Quasirung orthopair fuzzy logic and multi-criteria methods.Scientific reports · 2025Article
- The impact of digital technology on total factor productivity in manufacturing enterprises.Scientific reports · 2025Article
- Groundbreaking Technologies and the Biocontrol of Fungal Vascular Plant Pathogens.Journal of fungi (Basel, Switzerland) · 2025Review
- Trends of Nanobiosensors in Modern Agriculture Systems.Applied biochemistry and biotechnology · 2025Review
- The Use of Explainable Machine Learning for the Prediction of the Quality of Bulk-Tank Milk in Sheep and Goat Farms.Foods (Basel, Switzerland) · 2024Article
- Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images.Sensors (Basel, Switzerland) · 2024Article
- Impact of agricultural digitalization on carbon emission intensity of planting industry: Evidence from China.Heliyon · 2024Article
- Editorial: The application of phytohormones in plant biotechnology for sustainable agriculture.Frontiers in plant science · 2024Article
- Vegetated buffer strips for reducing diffused agricultural water pollution in Africa.Journal of environmental qualityReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the decades, agri-food security has become one of the most critical concerns in the world. Sustainable agri-food production technologies have been reliable in mitigating poverty caused by high demands for food. Recently, the applications of agri-food system technologies have been meaningfully changing the worldwide scene due to both external strengths and internal forces. Digital agriculture (DA) is a pioneering technology helping to meet the growing global demand for sustainable food production. Integrating different sub-branches of DA technologies such as artificial intelligence, automation and robotics, sensors, Internet of Things (IoT) and data analytics into agriculture practices to reduce waste, optimize farming inputs and enhance crop production. This can help shift from tedious operations to continuously automated processes, resulting in increasing agricultural production by enabling the traceability of products and processes. The application of DA provides agri-food producers with accurate and real-time observations regarding different features influencing their productivity, such as plant health, soil quality, weather conditions, and pest and disease pressure. Analyzing the results achieved by DA can help agricultural producers and scholars make better decisions to increase yields, improve efficiency, reduce costs, and manage resources. The core focus of the current work is to clarify the benefits of some sub-branches of DA in increasing agricultural production efficiency, discuss the challenges of practical DA in the field, and highlight the future perspectives of DA. This review paper can open new directions to speed up the DA application on the farm and link traditional agriculture with modern farming technologies.
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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.