SynthesisTheScientificWorldJournal2024
Application of Precision Agriculture Technologies for Sustainable Crop Production and Environmental Sustainability: A Systematic Review.
Synthesis in TheScientificWorldJournal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Challenges and Future Directives of Synthetic Biology in Engineering Plant-Microbe Partnerships for Sustainable Agriculture.Biotechnology and bioengineering · 2026Review
- Machine learning techniques for accessing the chlorophyll content and fluorescence in maize based on UAV multispectral sensor.BMC plant biology · 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
- GNN-ML-FRL: a graph-enhanced meta-adaptive federated learning framework for scalable pest identification and ernvironmental modeling.Scientific reports · 2026Article
- Greenhouse gas sequestration in poultry farming: Strategies for sustainable production and environmental impact mitigation.Poultry science · 2026Review
- Epigenetic modifications regulate peg elongation and underground fruiting in peanut in response to environmental cues.The plant genome · 2026Review
- AI-driven routing and layered architectures for intelligent ICT in nanosensor networked systems.iScience · 2026Review
- Intelligent fire detection in agriculture using machine learning and embedded systems for risk prevention and improved sustainability.Scientific reports · 2026Article
- AI-Driven Weather Data Superresolution via Data Fusion for Precision Agriculture.Sensors (Basel, Switzerland) · 2026Article
- Precision Farming with Smart Sensors: Current State, Challenges and Future Outlook.Sensors (Basel, Switzerland) · 2026Review
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
- How U.S. Federal Artificial Intelligence (AI) policy is shaping agrifood systems: an integrative review.Frontiers in artificial intelligence · 2026Article
- Self-calibrating neuromorphic system for adaptive environmental sensing.Frontiers in artificial intelligence · 2026Article
- Article
- Non-destructive estimation of maize carotenoids using reflectance-based spectral indices.Frontiers in plant science · 2026Article
- Microbial community characterization in semi-hydroponic systems of Starbor kale (Current research in microbial sciences · 2026Article
- Precise and Quantitative Chlorosis Severity Assessment Framework (PQCSAF) using evolutionary superpixels.Scientific reports · 2025Article
- Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture.Scientific reports · 2025Article
- Real-Time Object Detection for Edge Computing-Based Agricultural Automation: A Case Study Comparing the YOLOX and YOLOv12 Architectures and Their Performance in Potato Harvesting Systems.Sensors (Basel, Switzerland) · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision agriculture technologies (PATs) transform crop production by enabling more sustainable and efficient agricultural practices. These technologies utilize data-driven approaches to optimize the management of crops, soil, and resources, thus enhancing both productivity and environmental sustainability. This article reviewed the application of PATs for sustainable crop production and environmental sustainability around the globe. Key components of PAT include remote sensing, GPS-guided equipment, variable rate technology (VRT), and Internet of Things (IoT) devices. Remote sensing and drones deliver high-resolution imagery and data, enabling precise monitoring of crop health, soil conditions, and pest activity. GPS-guided machinery ensures accurate planting, fertilizing, and harvesting, which reduces waste and enhances efficiency. VRT optimizes resource use by allowing farmers to apply inputs such as water, fertilizers, and pesticides at varying rates across a field based on real-time data and specific crop requirements. This reduces over-application and minimizes environmental impact, such as nutrient runoff and greenhouse gas emissions. IoT devices and sensors provide continuous monitoring of environmental conditions and crop status, enabling timely and informed decision-making. The application of PAT contributes significantly to environmental sustainability by promoting practices that conserve water, reduce chemical usage, and enhance soil health. By enhancing the precision of agricultural operations, these technologies reduce the environmental impact of farming, while simultaneously boosting crop yields and profitability. As the global demand for food increases, precision agriculture offers a promising pathway to achieving sustainable crop production and ensuring long-term environmental health.
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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.