English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present two large datasets of labelled plant-images that are suitedtowards the training of machine learning and computer vision models. The firstdataset encompasses as the day of writing over 1.2 million images ofindoor-grown crops and weeds common to the Canadian Prairies and many USstates. The second dataset consists of over 540,000 images of plants imaged infarmland. All indoor plant images are labelled by species and we provide richetadata on the level of individual images. This comprehensive database allowsto filter the datasets under user-defined specifications such as for examplethe crop-type or the age of the plant. Furthermore, the indoor dataset containsimages of plants taken from a wide variety of angles, including profile shots,top-down shots, and angled perspectives. The images taken from plants in fieldsare all from a top-down perspective and contain usually multiple plants perimage. For these images metadata is also available. In this paper we describeboth datasets' characteristics with respect to plant variety, plant age, andnumber of images. We further introduce an open-access sample of theindoor-dataset that contains 1,000 images of each species covered in ourdataset. These, in total 14,000 images, had been selected, such that they forma representative sample with respect to plant age and ndividual plants perspecies. This sample serves as a quick entry point for new users to thedataset, allowing them to explore the data on a small scale and find theparameters of data most useful for their application without having to dealwith hundreds of thousands of individual images.

Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture