Artificial Intelligence in Agriculture Impact Factor

The current 16.1 JIF is up from 12.4, a 3.7-point increase. That is a real rise, but it is still a young-journal signal. Artificial Intelligence in Agriculture is expanding from a smaller citation base, so the current number should be read with the title, category, and scope rather than as a stand-alone prestige shortcut.

Source limitation: this is a JIF guardrail, not an invented 10-year impact-factor series. Use it alongside official guidance, and use JCR directly for formal historical rank, quartile, and category citations. The open Scopus-based impact-score curve can help show field momentum, but it should not be mixed with the Journal Impact Factor.

Artificial Intelligence in Agriculture is easy to misread because three public sources show different snapshots. The current KeAi journal page shows 16.1 and 29.1. ScienceDirect insights still shows the older 12.4 and 23.0. KeAi's 2025 announcement documents the 12.4 prior value and the older rank positions.

The title boundary also matters. The exact title is Artificial Intelligence in Agriculture, the eISSN is 2589-7217, the pISSN is 2097-2113, and the JCR abbreviation is ARTIF INTELL AGR. Do not mix it with the general phrase "artificial intelligence in agriculture" as a field, with MDPI Agriculture AI-policy pages, or with broader agricultural-engineering journals.

The 16.1 JIF says Artificial Intelligence in Agriculture is no longer a novelty outlet. It has become a visible Q1 owner for work at the AI-and-agriculture intersection. The metric is especially notable because the journal is not a general AI venue. KeAi describes the journal as an open-access forum for artificial intelligence in agriculture, food, and bio-system engineering, including decision support, precision agriculture, sensors, robotics, machine vision, machine learning, remote sensing, and agricultural optimization.

For authors, the number should change the shortlist only when the manuscript has two real centers of gravity. A strong paper for this journal needs a credible AI contribution and a concrete agricultural, food-system, or bio-system consequence. A crop-image classifier with a standard model and a thin agricultural explanation may look attractive because of the 16.1 JIF, but it can still be a poor fit if the agricultural decision problem is not central.

The current metric also explains why the journal is not an easy backup route. The front-end timeline is fast, but the journal is efficient because the fit screen is narrow. KeAi's author guide says the journal uses single-blind review, with an initial editor suitability assessment and typically at least two independent reviewers for papers that clear that first screen. In practice, the metric should make authors more careful, not less careful, about proving the dual contribution before upload.

In our pre-submission review work on Artificial Intelligence in Agriculture manuscripts, three patterns explain why a high-metric target still fails to fit. These are manuscript-component patterns, not generic warnings about "quality."

Manusights guide-build evidence units for this page are the current metric-conflict ledger and the agricultural-AI manuscript patterns we see when authors use a strong impact factor before proving journal fit.

Artificial Intelligence in Agriculture method-first abstract. We repeatedly see abstracts where the first sentence names a model family, architecture, or benchmark, and the agricultural problem arrives only after the result. For this journal, that sequence can misroute the paper. The editor needs to see the agricultural, food-system, or bio-system decision problem before the model performance becomes meaningful. The repair is visible in the title, abstract, graphical abstract, first figure, and cover letter: lead with the agricultural decision, then explain why AI was necessary.

Artificial Intelligence in Agriculture benchmark without an agricultural baseline. A model can beat another neural network and still fail the journal-fit test if it never compares against a simpler agronomic, sensor, rule-based, or statistical alternative. The issue is not that complex AI is unwelcome. The issue is that the manuscript has to show why the AI method changes prediction, management, automation, input use, disease monitoring, animal welfare, yield estimation, or food-system control in a way a simpler method does not.

Artificial Intelligence in Agriculture validation that is too clean for the claim. The strongest claims often depend on field noise, domain shift, sensor variability, cultivar differences, animal or crop context, geography, weather, and operational constraints. A paper that works only on a clean curated dataset can still be useful, but the limitations must be honest. In this journal, we look for dataset provenance, train-test separation, external or field-relevant validation, code availability, and a limitations section that names where the model may fail.

The useful repair is concrete: rewrite the abstract conclusion, graphical abstract, first figure caption, dataset/code statement, and cover letter so they all name the same agricultural decision problem. If those components point in different directions, the 16.1 JIF is a distraction.

A Artificial Intelligence in Agriculture fit check can test whether the abstract, graphical abstract, methods, validation, data/code statement, and cover letter support this target before upload.

Verify the current Editor-in-Chief on the journal's editorial-board page before quoting any name in a submission cover letter.

Artificial Intelligence in Agriculture at 16.1 is a strong current metric signal. It is Q1-positioned, has a very high current CiteScore on KeAi's page, and now sits above several adjacent agricultural-technology venues by JIF. That makes it attractive, but also less forgiving.

The best use of the number is to ask whether the paper genuinely owns both halves of the journal name. If the AI method is central and the agricultural consequence is concrete, the metric supports the target. If the paper is mostly a machine-learning benchmark that happens to use agricultural data, the 16.1 JIF makes the journal look better than the fit really is.