The purpose of this article consists of analyzing publications discussing the use of agent-based artificial intelligence models in sustainable agriculture research. The analysis involved bibliometric indicators and the Rstudio software with Bibliometrix library. The methodology is descriptive with a quantitative approach. Scientific databases SCOPUS and Web of Science were consulted and the PRISMA methodology was used during the selection process. This led to finding 86 publications that met the inclusion criteria. Amongst the results, United States was listed as the country with the highest production of scientific material, although France had a higher impact. Additionally, the bibliographical resources that help promote scientific development are open source. It was concluded that the agent-based model has been adopted to simulate different scenarios, which help decision-makers to formulate public policies in favor of sustainable agriculture. This optimizes the use of natural resources and reduces negative consequences for the environment, while also delivering value for the stakeholders of the agricultural system.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720568 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2022.e12005 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Examining ozone effects on the tropical C crop .
PeerJ
January 2025
College of Science & Engineering and Centre for Tropical Environmental and Sustainability Science, James Cook University of North Queensland, Cairns, Queensland, Australia.
Ozone (O), a major air pollutant, can negatively impact plant growth and yield. While O impacts have been widely documented in crops such as wheat and soybean, few studies have looked at the effects of O on sorghum, a C plant and the fifth most important cereal crop worldwide. We exposed grain sorghum ( cv.
View Article and Find Full Text PDFInvestigating the Effect of Syringe Infiltration on (Tobacco).
ACS Agric Sci Technol
January 2025
Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, SE-60174 Norrköping, Sweden.
Plant infiltration techniques, particularly agroinfiltration, have transformed plant science and biotechnology by enabling transient gene expression for genetic engineering of plants or genomic studies. Recently, the use of infiltration has expanded to introduce nanomaterials and polymers in plants to enable nonnative functionalities. Despite its wide use, the impact of the infiltration process on plant physiology needs to be better understood.
View Article and Find Full Text PDFSmartphone image dataset for radish plant leaf disease classification from Bangladesh.
Data Brief
February 2025
Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Radishes, which are common root vegetables, are rich in vitamins and minerals, and contain low calories. This vegetable is known for its rapid growth. Nevertheless, the variety of leaf diseases where leaves get affected by various bacterial and fungal diseases can hinder the healthy growth of radish.
View Article and Find Full Text PDFEffect of operational parameters on droplet deposition characteristics using an unmanned aerial vehicle for banana canopy.
Front Plant Sci
January 2025
College of Engineering, South China Agricultural University, Guangzhou, China.
In recent years, as an important part of precision agricultural aviation, the plant protection unmanned aerial vehicle (UAV) has been widely studied and applied worldwide, especially in East Asia. Banana, as a typical large broad-leaved crop, has high requirements for pests and diseases control. The mechanization degree of plant protection management in banana orchard is low.
View Article and Find Full Text PDFAdvancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring.
Front Plant Sci
January 2025
College of Information Technology, Jilin Agricultural University, Changchun, China.
Introduction: Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!