# An Image Labeling Tool and Agricultural Dataset for Deep Learning

Source: [An Image Labeling Tool and Agricultural Dataset for Deep Learning](<https://www.sota2.com/research/paper/68fa67511e209131cea156b8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:01:46.445Z

Authors: Patrick Wspanialy, Justin Brooks, Medhat Moussa

Published: 2020-04-06T13:38:01.000Z

## Abstract

We introduce a labeling tool and dataset aimed to facilitate computer vision research in agriculture. The annotation tool introduces novel methods for labeling with a variety of manual, semi-automatic, and fully-automatic tools. The dataset includes original images collected from commercial greenhouses, images from PlantVillage, and images from Google Images. Images were annotated with segmentations for foreground leaf, fruit, and stem instances, and diseased leaf area. Labels were in an extended COCO format. In total the dataset contained 10k tomatoes, 7k leaves, 2k stems, and 2k diseased leaf annotations.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.03351>)

[PDF](<https://arxiv.org/pdf/2004.03351.pdf>)