Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices
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In general, agriculture plays a crucial role in human survival as a primary source of food, alongside other sources such as fishing. Unfortunately, global warming and other environmental issues, particularly in less privileged nations, hamper the Agricultural sector. It is estimated that a range of 720 to 811 million individuals experienced food insecurity. Today's agriculture faced significant difficulties and obstacles, as do the surveillance and monitoring systems (climate, energy, water, fields, works, cost, fertilizers, diseases, etc.). The COVID-19 pandemic has exacerbated the susceptibilities and insufficiencies inherent in worldwide food systems. Current agricultural practices tend to prioritize productivity and profitability over environmental conservation and long-term sustainability. To establish sustainable agriculture capable of meeting the needs of a projected ten billion people in the next 30 years, substantial structural and automation changes are required. However, these obstacles can be overcome by employing smart technologies and advancing Artificial Intelligence (AI) in agricultural operations. AI is believed to contribute to global sustainability goals in multiple sectors, particularly in the incorporation of renewable energy. It is anticipated that AI will revitalize both existing and new agricultural fields by retrofitting, installing and integrating automatic devices and instruments. This paper presents a comprehensive review of the most promising and novel applications of AI in the agriculture industry. Furthermore, the role of AI in the transition to sustainability and precision agriculture is investigated.
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XAI aids in harvest prediction by analyzing historical data, crop maturity levels, and environmental factors to forecast optimal harvest times, maximizing yield and minimizing waste.
“Mana et al. [102], explain that XAI aids in harvest prediction by analyzing historical data, crop maturity levels, and environmental factors to forecast optimal harvest times, maximizing yield, and minimizing waste.”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [102]
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Flagged first, then by citations
Cited by
Sorted by citations
Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11732-2 · Cited by 168 · see citing works
- Authors
- Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K.
- Affiliations
- Florida International University (US)
- King Fahd University of Petroleum and Minerals (SA)
- Corresponding author
- Mana, A.A.
- Venue
- Smart Agricultural Technology
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 7
- Pages
- 100416
- ISSN
- 2772-3755 (ISSN-L)
- OpenAlex
- W4391900141
- Cited by
- 245
- References
- 166
- Citations by year
- 2026: 87 · 2025: 122 · 2024: 36
- FWCI
- 59.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Smart Agricultural Technology (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- artificial intelligence in agriculture, sustainable agriculture, precision agriculture, agricultural automation, smart agriculture, food security, climate change impacts on agriculture, renewable energy in agriculture
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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