Description, vertus et usages de sept cents dix-neuf plantes - tant étrangeres que de nos climats; et de cent trente-quartre animaux, en sept cents trente planches, gravées en taille-douce, sur les (14781223684)
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Identifier: descriptionvertu02geof (find matches)
Title: Description, vertus et usages de sept cents dix-neuf plantes : tant étrangeres que de nos climats ; et de cent trente-quartre animaux, en sept cents trente planches, gravées en taille-douce, sur les desseins dʼaprès nature, de M. de Gersault, par MM. de Fehrt, Prevost, Duflos, Martinet, &c. et rangées suivant lʼordre du livre intitulé Matiere médicale de M. Geoffroy ..
Year: 1767 (1760s)
Authors: Geoffroy, Etienne-François, 1672-1731 Garsault, François A. de (François Alexandre), 1693-1778
Subjects: Materia medica Medicinal plants
Publisher: Paris : P.F. Didot le jeune
Contributing Library: Harvard Botany Libraries
Digitizing Sponsor: BHL-SIL-FEDLINK
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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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