Artificial Intelligence in Agriculture Submission Process

The early window is an editor scope screen for the dual contribution. For papers sent to reviewers, the journal publishes accepted research in about 12 weeks on average, while edge cases diverge sharply: a generic-machine-learning paper without agricultural justification is an expedited scope return in the first 7 to 14 days, and a dual-contribution paper sent to two reviewers is an outlier that runs toward the 12-week average and beyond with revisions. Artificial Intelligence in Agriculture is the Elsevier and KeAi gold-open-access venue at the AI and agriculture intersection, and the dual-contribution screen is the dominant feature of the early timeline.

If you want an outside read before you open Editorial Manager, use the free manuscript readiness check to test whether the agricultural justification survives the scope screen.

Initial quality check: completeness and policy fit

The first layer is administrative but still decisive. Before the editor reads for the dual contribution, Editorial Manager verifies authorship and contributor roles, competing-interest and funding disclosure, ethics statements where field, animal, or human data are involved, and the dataset and code availability statement, alongside the graphical abstract. A submission can look finished in the portal and still be returned if the title, abstract, and graphical abstract do not show why the AI method solves a concrete agricultural problem.

Editorial assignment: routing by problem area

Artificial Intelligence in Agriculture routes to an editor matched to the problem area (crop and soil, precision agriculture, plant and animal phenotyping, food-system engineering, or bio-system modeling). The framing you signal in the title and abstract determines which editor reads the contribution first, and a method-first framing without an agricultural problem can read as a general AI paper.

Peer review: dual-contribution assessment after the scope screen

Manuscripts that clear the scope screen move to at least two independent reviewers under single-blind review. The reviewer job is not only to check that the model works. It is to decide whether the AI method is technically credible, whether it solves a concrete agricultural problem better than a simpler approach, and whether the dataset, validation, and code support the claim.

Final decision: dual contribution stays live after reports return

Even after review, the decision still turns on the dual contribution. A technically sound model can be returned if the reports show the agricultural justification is thin, the result is a benchmark exercise, or a simpler method would have done as well without the AI framing.

This is the early stage that decides whether the paper reaches review. Before a reviewer is assigned, the editor reads the title, the abstract, the graphical abstract, and the cover letter, and decides whether the manuscript proves a dual contribution.

At this stage the editor is effectively asking:

• is the AI method credible, and does it address something specific about agriculture?

• does the work solve a concrete agricultural, food-system, or bio-system problem better than a simpler approach?

• is the package complete, with a dataset and code statement and a graphical abstract that shows the agricultural problem?

Because this screen is selective, an early return is usually a dual-contribution scope return, not a technical rejection. The most consistent trigger is generic machine learning applied to agricultural data without an agricultural justification.

Papers that clear the scope screen go to at least two reviewers, who typically assess:

• whether the AI method is technically credible

• whether it solves a concrete agricultural problem better than a simpler approach

• whether the dataset and validation support the claim

• whether the code and reproducibility are adequate

• clarity of the agricultural problem in the abstract and graphical abstract

Artificial Intelligence in Agriculture uses single-blind review, so reviewers see author identities while staying anonymous themselves, and accepted papers publish in about 12 weeks on average. A single manuscript can move faster or slower depending on reviewer availability and revision rounds.

• Reject (early, scope): an editor scope return, usually because the agricultural justification is missing and the work reads as a benchmark exercise. Strengthen the agricultural justification or re-route to a general AI or agriculture venue before resubmitting.

• Major revision: substantive reviewer concerns, often about the agricultural justification, the dataset, or whether a simpler method would suffice. The revised paper usually returns to the same reviewers; respond point by point.

• Minor revision: the paper is essentially accepted pending specific fixes. Respond carefully and promptly.

• Accept: uncommon on the first round; usually follows a clean revision.

Four recurring patterns return otherwise-capable Artificial Intelligence in Agriculture packages at the scope screen:

• Generic machine learning with a farming mention. A standard architecture applied to agricultural data without explaining why the AI approach addresses something specific about agriculture is the most consistent desk-rejection trigger.
• No comparison to a simpler approach. A model that does not show it beats a simpler method reads as a benchmark exercise, not an agricultural contribution.
• A method-first framing. A title and abstract that lead with the architecture and reach agriculture late route the paper toward a general AI reading.
• A missing dataset, code, or graphical abstract. An incomplete package, or a graphical abstract that does not show the agricultural problem, is an avoidable early return.

Check whether your Artificial Intelligence in Agriculture abstract shows the agricultural justification to the editor →

Check if your Artificial Intelligence in Agriculture dataset and code package is complete before the scope screen →

Check whether your manuscript proves a dual AI-and-agriculture contribution →

This guide tells you what Artificial Intelligence in Agriculture editors look for at the scope screen; the review tells you whether your paper passes the dual-contribution bar. 60-day money-back guarantee; authors retain all rights and we do not train models on submitted manuscripts.

In our pre-submission review work on Artificial Intelligence in Agriculture submissions, three patterns account for most of the manuscripts that stall at the scope screen, before a reviewer is ever assigned.

The agricultural justification is missing

We repeatedly see Artificial Intelligence in Agriculture manuscripts where a standard deep-learning architecture is applied to agricultural data without explaining why the particular AI approach addresses something specific about agriculture. Because the editor reads the abstract and graphical abstract for the dual contribution, a model without an agricultural justification reads as a benchmark exercise. The fix we push is to state, in the abstract, the agricultural problem and why the AI method solves it better than a simpler approach.

The comparison to a simpler method is absent

A related pattern is a strong model that never shows it beats a simpler baseline on the agricultural task. Because reviewers ask whether the AI was necessary, a missing comparison reads as an unjustified use of a complex method. We help authors add the simpler-method comparison and report it in the abstract and figures, because that comparison is what turns a benchmark exercise into an agricultural contribution.

The framing is method-first

The third pattern is a title and abstract that lead with the architecture and reach the agricultural problem only in the methods or discussion. The Artificial Intelligence in Agriculture editor reads a method-first paper as a general AI submission, and we help authors lead with the agricultural problem and the dataset, then present the AI method as the solution, so the dual contribution is legible before the scope screen reaches the code. In our Artificial Intelligence in Agriculture readiness checks we confirm the abstract and graphical abstract state the agricultural problem, the results compare the AI method to a simpler baseline, and the dataset and code statement is complete, because those are the components the editor reads before the model is judged on its merits.

Artificial Intelligence in Agriculture (Elsevier and KeAi, JIF 12.4, gold open access) sits among several adjacent venues, and the scope screen is partly a routing decision:

• choose Computers and Electronics in Agriculture for an agricultural-engineering computing contribution with a different framing

• choose a general AI or machine-learning venue when the contribution is a method advance rather than an agricultural one

• choose a general agriculture or food-science journal when the agricultural finding matters more than the AI method

• stay with Artificial Intelligence in Agriculture when the work proves a dual contribution: credible AI that solves a concrete agricultural problem better than a simpler approach

Last verified June 2026 against the journal's KeAi and ScienceDirect author materials and the Editorial Manager intake. Editorial timing and policies shift between updates; treat the numbers as planning ranges and confirm the current figures before you submit.