Copernica ceifera - Public domain illustration drawing
Summary
Botanical drawings collection from various sources selected by BibliOdyssey.
Public domain scan of 19th-century botanical illustration, free to use, no copyright restrictions image - Picryl description
Natural history. Botanical drawings, hand-colored engravings and book prints from various sources.
A profession of botanical illustrator began to emerge in the eighteenth century with advances in the printing processes. Botanical Illustrations became accurate in color and detail. Amateur botanists, gardeners, and natural historians provided a market for botanical publications. The photographic process has not made botanical illustrations obsolete since illustrators were able to combine accuracy, an idealized image from several specimens, and the inclusion of the face and reverse of the features such as leaves with details given at a magnified scale.
This large AI-assisted collection comprises about 60,000 images of botanical drawings and illustrations. It spans from the 14th to 19th century. As of today, we estimate the total number of botanical illustrations in our archive as 200,000 and growing. The "golden age" of botanical illustration is generally considered to be the 18th and 19th centuries, a time when there was a great deal of interest in botany and a proliferation of botanical illustrations being produced. During this period, many of the great botanical illustrators of the time, such as Maria Sybilla Merian, Pierre-Joseph Redouté, and John James Audubon, were active and produced some of the most iconic and influential botanical illustrations of all time. In addition to being used for scientific purposes, botanical illustrations were also highly prized for their beauty and were often used to decorate homes and other public spaces. Many of the most famous botanical illustrations from this period are still admired and collected today for their beauty and historical significance. All large Picryl collections were made possible with the development of neural image recognition. We made our best to reduce false-positive image recognition to under 5%.
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