The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Understanding the potential applications of Artificial Intelligence in Agriculture Sector
Open accessAbstract
Artificial Intelligence (AI) has been extensively applied in farming recently. To cultivate healthier crops, manage pests, monitor soil and growing conditions, analyse data for farmers, and enhance other management activities of the food supply chain, the agriculture sector is turning to AI technology. It makes it challenging for farmers to choose the ideal time to plant seeds. AI helps farmers choose the optimum seed for a particular weather scenario. It also offers data on weather forecasts. AI-powered solutions will help farmers produce more with fewer resources, increase crop quality, and hasten product time to reach the market. AI aids in understanding soil qualities. AI helps farmers by suggesting the nutrients they should apply to increase the quality of the soil. AI can help farmers choose the optimal time to plant their seeds. Intelligent equipment can calculate the spacing between seeds and the maximum planting depth. An AI-powered system known as a health monitoring system provides farmers with information on the health of their crops and the nutrients that need to be given to enhance yield quality and quantity. This study identifies and analyses relevant articles on AI for Agriculture. Using AI, farmers can now access advanced data and analytics tools that will foster better farming, improve efficiencies, and reduce waste in biofuel and food production while minimising the negative environmental impacts. AI and Machine Learning (ML) have transformed various industries, and the AI wave has now reached the agriculture sector. Companies are developing several technologies to make monitoring farmers' crop and soil health easier. Hyperspectral imaging and 3D laser scanning are the leading AI-based technologies that can help ensure crop health. These AI-powered technologies collect precise data on the health of the crops in greater volume for analysis. This paper studied AI and its need in Agriculture. The process of AI in Agriculture and some Agriculture parameters monitored by AI are briefed. Finally, we identified and discussed the significant applications of AI in agriculture.
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Citations
Cites
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Artificial intelligence, systemic risks, and sustainability
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., et al. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101741 · Cited by 497
Computational Sustainability and Artificial Intelligence in the Developing World
Quinn, J., Frias‐Martinez, V., & Subramanian, L. (2014) · AI Magazine · doi:10.1609/aimag.v35i3.2529 · Cited by 31
Role of Artificial Intelligence and the Internet of Things in Agriculture
Singh, G., Singh, A., & Kaur, G. (2021) · Artificial Intelligence to Solve Pervasive Internet of Things Issues · doi:10.1016/b978-0-12-818576-6.00016-2 · Cited by 12
Cited by
Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245
The impact of 6G-IoT technologies on the development of agriculture 5.0: a review
Polymeni, S., Plastras, S., Skoutas, D. N., Kormentzas, G., & Skianis, C. (2023) · Electronics · doi:10.3390/electronics12122651 · Cited by 121
Hao, X., & Demir, E. (2024) · Journal of Modelling in Management · doi:10.1108/jm2-01-2023-0009 · Cited by 92
Metadata
- Authors
- Javaid, M., Haleem, A., Khan, I. H., & Suman, R.
- Venue
- Advanced Agrochem
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 2
- Issue
- 1
- Pages
- 15-30
- ISSN
- 2773-2371 (ISSN-L)
- OpenAlex
- W4307486628
- Cited by
- 862
- References
- 4
- FWCI
- 80.76
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem. https://doi.org/10.1016/j.aac.2022.10.001
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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