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Delve into our recent articles, offering a diverse range of topics, expert perspectives, and thought-provoking ideas across various fields.
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Major Plant.id model upgrade
We boosted the number of classes from 12k to 33k and increased the accuracy!
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Major Plant.id model upgrade
We boosted the number of classes from 12k to 33k and increased the accuracy!
Weed and crop identification for a weeder
We have recently paired up with a bunch of companies from Czechia to work on a smart weeder.
Meet the new Mushroom ID!
The new API can identify 3,100 species of fungi (including lichens and related organisms such as slime molds) and includes enhanced information that has been carefully selected and prepared based on the needs of people interested in mushroom identification.
Do The Different Light Conditions Have An Influence On Plant.Id Identification Accuracy?
Plant.id is an API that identifies plant species and diseases from photos with machine learning. Send us images of your plant and get the possible suggestions with plenty of other information including representative images of the species.
Insect.id API beta release is here!
The new API enables you to identify species of terrestrial invertebrates, including insects and spiders from images using a machine-learning model. It is easy to use and we already love how it works.
We Can Estimate The Yield In An Apple Tree Orchard
We have developed a machine learning solution that can identify and quantify flower clusters from photos. This idea came from a Polish farmer’s need for apple yield estimation.
Use Plant.Id Demo As An App
The primary purpose of Plant.id is to provide a plant species and diseases identification API. To make it accessible to non-programmers, we created a web demo, which you can try at https://plant.id/ website.
Health Assessment Production Release
Let us introduce you to the first production version of Plant.id Health Assessment. During the last year, we have been collecting feedback on the beta version of our plant disease identification API. Moreover, we have expanded our datasets and deepened our understanding of machine learning-powered plant disease identification.