Artificial Intelligence in Agriculture Impact Factor
Artificial Intelligence in Agriculture impact factor is 12.4 with CiteScore 23.0. See the trend, SJR, and what that means.
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Quick answer: Artificial Intelligence in Agriculture currently lists an official impact factor of 12.4 and an official CiteScore of 23.0 on its ScienceDirect insights page. That is a strong citation profile for a young specialist journal. The practical point is that this is not a home for generic machine-learning work with a farm dataset attached. It is a journal with a high bar for papers where AI and agricultural consequence are both real.
Artificial Intelligence in Agriculture impact metrics at a glance
Metric | Value |
|---|---|
Official Impact Factor | 12.4 |
Official CiteScore | 23.0 |
Scopus impact score 2024 | 18.77 |
SJR 2024 | 1.75 |
h-index | 34 |
Best quartile | Q1 |
Overall rank | 1816 |
Official submission to first decision | 17 days |
Official submission to decision after review | 113 days |
Official submission to acceptance | 254 days |
Official acceptance to online publication | 6 days |
Publisher | KeAi / Elsevier |
That profile is unusually strong for a relatively new journal and signals that the venue is being treated seriously by both AI and agriculture researchers.
What 12.4 actually tells you
The first signal is that the journal has moved beyond novelty status. A 12.4 JCR impact factor and 23.0 CiteScore say the field now treats it as a meaningful owner for papers at the agriculture-plus-AI intersection.
The second signal is editorial identity. The journal's official aims and scope are explicit that it serves AI applications in agriculture, food, and bio-system engineering. That matters because the metrics are not being earned by general AI papers. They are being earned by papers that connect technical methods to domain-specific problems.
The third signal is competitive selectivity. KeAi's own announcement about the journal's first impact factor highlighted a high rank in both Agriculture, Multidisciplinary and Computer Science, Artificial Intelligence. That is a useful clue about how the market sees it: not as a novelty outlet, but as a serious bridge journal.
That is why the number should not be read as "easy high-impact target." It should be read as "strong specialist venue with a very specific fit screen."
Artificial Intelligence in Agriculture impact factor trend
The ScienceDirect insights page is the authoritative source for the current impact factor and CiteScore on this page. For the longer directional view, the table below uses the open Scopus-based impact-score series as a trend proxy.
Year | Scopus impact score |
|---|---|
2019 | 0.00 |
2020 | 31.00 |
2021 | 24.67 |
2022 | 10.26 |
2023 | 12.63 |
2024 | 18.77 |
Directionally, the open Scopus-based signal is up from 12.63 in 2023 to 18.77 in 2024. Because this is still a young journal, the year-to-year curve is more volatile than it would be for an older archive. The important point is not the early spike alone. It is that the journal is still running at a high level after the launch-phase volatility.
Why the number can mislead authors
The common mistake is to see a strong impact factor and assume any AI paper with crop images, farm sensors, or agricultural tables belongs here.
That is usually wrong.
This journal still expects:
- a real agricultural problem
- an AI method that is genuinely necessary to the solution
- validation under conditions that feel credible for the agricultural setting
- a clear explanation of what the performance gain changes in practice
So a technically good AI paper can still be the wrong fit if the agricultural consequence is weak or the validation design is unrealistic.
How this journal compares with nearby choices
Journal | Best fit | When it beats this journal | When this journal is stronger |
|---|---|---|---|
Artificial Intelligence in Agriculture | AI work with real agricultural ownership | When the manuscript is tightly about AI in agricultural systems | When the paper needs both agriculture and AI readers |
Computers and Electronics in Agriculture | Broader digital-agriculture and engineering work | When the study is more systems-engineering or sensing-driven than AI-centered | When AI is the real intellectual center |
Precision Agriculture | Agronomic decision support and field optimization | When the contribution is more agronomy and management focused | When the paper is more explicitly about AI methods and evaluation |
Biosystems Engineering | Applied engineering for agriculture | When the paper is more device, systems, or process engineering than AI | When the paper's value comes from AI modeling and inference |
That comparison matters because the journal can look attractive on metrics while still being the wrong owner for a paper whose real audience sits somewhere else.
What pre-submission reviews reveal about manuscripts aimed here
In our pre-submission review work with manuscripts aimed at Artificial Intelligence in Agriculture, four patterns recur.
The paper is really benchmark AI with agricultural labels. Editors in this lane want domain consequence, not just score improvements.
Validation is too controlled. If the manuscript only works on a clean curated dataset and never addresses field noise, domain shift, or operational limits, the practical case weakens quickly.
The agricultural problem is under-explained. A strong model section cannot rescue a paper that never makes the real farm, food, or bio-system problem legible.
The paper reports metrics but not decisions. At this journal level, authors need to explain what the gain changes for prediction, management, automation, or resource use.
If that sounds familiar, an agricultural AI readiness check is usually more useful than more cosmetic revision.
The information gain that matters here
The ScienceDirect insights page adds useful non-IF signals beyond the headline metrics.
Official signal | Value | Why it matters |
|---|---|---|
Submission to first decision | 17 days | Front-end editorial triage is reasonably fast |
Submission to decision after review | 113 days | Reviewed manuscripts still face a substantial technical screen |
Submission to acceptance | 254 days | The journal is selective enough that revision depth can be meaningful |
APC | USD 1,100 | Lower friction than many comparable open-access venues |
That timing pattern tells authors something important: the journal is efficient at the front end, but it is not a lightweight path. Strong papers still need to survive a real review process.
How to use this number in journal selection
Use the impact factor to place the journal correctly. This is now a serious Q1 owner in agricultural AI.
Then ask the harder question: if the farm or food-system context were removed, would the paper still obviously belong here?
That usually means checking whether the manuscript:
- addresses a real agricultural decision or constraint
- shows why AI is needed rather than merely available
- validates performance under believable conditions
- connects technical gains to agricultural value
If the answer is yes, the metrics support the target. If the answer is no, the number is flattering the fit.
What the number does not tell you
The impact factor does not tell you whether the agricultural ownership is strong enough, whether the evaluation is realistic enough, or whether the better home is a broader engineering or agronomy journal.
Those are the real editorial screens.
Submit if / Think twice if
Submit if:
- the agricultural problem is consequential and explicit
- the AI method is genuinely necessary to the solution
- the validation is credible under realistic conditions
- the paper explains what changes for agricultural users or systems
Think twice if:
- the paper is mainly a benchmark exercise
- the farming context feels cosmetic
- the evaluation ignores practical constraints
- a broader engineering, agronomy, or sensing journal better matches the real contribution
Bottom line
Artificial Intelligence in Agriculture has an official impact factor of 12.4 and an official CiteScore of 23.0. The stronger signal is the combination of those metrics with a sharply defined agriculture-plus-AI editorial identity.
If the paper is not really about AI changing agricultural understanding or decisions, the metric will make the fit look better than it is.
Frequently asked questions
Artificial Intelligence in Agriculture currently lists an official impact factor of 12.4 on its ScienceDirect insights page, alongside an official CiteScore of 23.0.
Yes. It has quickly become a strong Q1 journal at the intersection of agriculture and AI. The more useful signal is the combination of a high official impact factor, a very strong CiteScore, and a clear domain-specific editorial identity.
No. The journal still expects real agricultural consequence and a genuine AI contribution. Generic ML benchmarking with a thin farming wrapper is still the wrong fit.
The common misses are benchmark-driven papers without enough agricultural realism, weak validation under practical conditions, and manuscripts that never explain what the technical gain changes for real agricultural systems.
Authors should also use the official CiteScore and timeline. For this journal, agricultural relevance and implementation credibility matter almost as much as the citation metrics.
Sources
Reference library
Use the core publishing datasets alongside this guide
This article answers one part of the publishing decision. The reference library covers the recurring questions that usually come next: whether the package is ready, what drives desk rejection, how journals compare, and what the submission requirements look like across journals.
Checklist system / operational asset
Elite Submission Checklist
A flagship pre-submission checklist that turns journal-fit, desk-reject, and package-quality lessons into one operational final-pass audit.
Flagship report / decision support
Desk Rejection Report
A canonical desk-rejection report that organizes the most common editorial failure modes, what they look like, and how to prevent them.
Dataset / reference hub
Journal Intelligence Dataset
A canonical journal dataset that combines selectivity posture, review timing, submission requirements, and Manusights fit signals in one citeable reference asset.
Dataset / reference guide
Peer Review Timelines by Journal
Reference-grade journal timeline data that authors, labs, and writing centers can cite when discussing realistic review timing.
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