Agri-FM+: a Self-Supervised Foundation Model for Agricultural Vision
Bibliographic record
Abstract
Foundation models have revolutionized computer vision, yet their adoption in precision agriculture remains limited due to significant domain shifts from natural images. Existing agricultural foundation models focus primarily on remote sensing applications; to date, no dedicated foundation model exists for close-field agricultural vision. In this paper, we propose Agri-FM+, a self-supervised foundation model specifically tailored for agricultural vision, trained via a two-stage continual learning pipeline. Starting from publicly available unsupervised ImageNet weights from the SlotCon, Agri-FM+ is continually adapted on a curated$147 K$-image agricultural dataset using SlotCon. Evaluated across eight diverse benchmarks-covering object detection, semantic segmentation, and instance segmentation tasks-Agri-FM+ consistently outperforms both ImageNet-pretrained and randomly initialized models. Under full supervision, it achieves average gains of$+1.27 \%$over supervised ImageNet-pretrained and +8.25% over random initialization. Even when trained with only 10 % of the annotated data, Agri-FM+ maintains robust performance, achieving gains of$+1.02 \%$and$+4.54 \%$over supervised ImageNet pretraining and random initialization, respectively. These results demonstrate the ability of Agri-FM+ to provide domain-adapted, label-efficient representations that scale effectively across real-world agricultural vision tasks. The code, weights, and more details will be made available at: https://github.com/FarhadMaleki/AgriFMPlus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".