Repository Overview

Welcome to the National Agricultural Image Repository (NAIR), a collection of images designed for computer vision and artificial intelligence research in the field of weed management. The need for effective weed management in agriculture is growing as the demand for food and fiber increase, as incidence of herbicide resistance rises, and as the call for more sustainable methods grows louder. In response, researchers and developers are turning to computer vision algorithms to help automate the detection and identification of weeds in agricultural fields.

This dataset was created to support this research by providing a diverse and large collection of labeled images that represent different types of weeds, cover crops, and crops, grown in different settings and growing conditions. A semi-automatic approach that uses photogrammetry, classical digital image processing, and deep learning has been used to generate multiple type of labels, capturing information about the plants present in the image, and making them suitable across various machine learning tasks. Here, you will find a comprehensive overview of the dataset, including its contents, annotation process, metadata, characteristics, and availability.

Makeup

NAIR is made-up of 3 broad plant datasets; Weeds (WIR), Cover crops (CCIR), and Cash crops (CIR) each of which can be further divided into two main scene types, Semi-field and true real-world Field each providing a unique set of advantages and use cases.

The datasets also include metadata captured during collection and throughout the processing pipeline. This information can be used to further understand the diversity of the dataset, deficiencies, general makeup, and the types of conditions that the algorithms are exposed to.

In summary, the weed image dataset is a comprehensive collection of images and metadata designed to support research in the field of weed management. The diverse and large dataset provides a wealth of data for computer vision algorithms to learn from, helping to improve the accuracy and efficiency of automatic weed detection in agricultural fields.

An Agricultural Image Repository for the United States

For this repository to be comprehensive and include most plant species of agricultural relevance for the different regions in the US, partnering with institutions throughout the country was key. There are nine states and ten institutions participating in data collection for the AgIR in 2023 (Figure 1). These partnerships allow us to gather images of the plant species growing in different environments, hence capturing the variability that it can introduce in morphology, and facilitates image collection particularly of weeds which can be challenging to grow at will.

Plant Species of Agricultural Relevance for the US

The list of target plant species for 2022 and 2023 includes the most common cover crop and cash crop species for the US. Moreover, the weed species list was defined by the participating partners to whom we asked to provide the names of the 4 most relevant weed species in their regions. The current list of species includes 44 weed species, 15 cover crop species and 3 cash crop species (Table 1).

Table 1. Cover crop, cash crop and weed species included in the AgIR in 2023

Semi-Automatic Labeling

A major component of NAIR and the semi-automatic labeling approach is the development of plant segments, or cutouts, that can be used to develop temporary datasets of synthetic images for training annotation assistant detection and segmentation models. Synthetic data along with weak image labels is used to iteratively refine whole image labels.