A novel dataset and deep learning object detection benchmark for grapevine pest surveillance.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

1. Fine-Grained Recognition of Insect Pests from Digital Images: A Survey.Neotropical entomology · 2026
   Pooled it
2. YOLOv8 forestry pest recognition based on improved re-parametric convolution.Frontiers in plant science · 2025
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

7 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

Indexed as