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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 images have been annotated with information about the location and type of agronomic plant present in the image. The annotations were created using a combination of manual labeling and computer vision algorithms, ensuring high accuracy and consistency.The datasets also include metadata about each the conditions under which the images were captured, including the type of soil, weather conditions, and the stage of growth of the weedscaptured 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.
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