plant.id

We started looking for synergies between our narrow ML models and LLMs. Let's play with this together on our demo 🙂

Multimodal Large Language models have recently made it possible to identify various objects from images, including plants. This became a competition for narrowly focused neural network models, such as that of Plant.id. Here we compare the accuracy of these models by focusing on the proportion of wrong answers.

Plant.id emerged as the leading application for the automatic identification of tree species, outperforming other methods in accuracy and efficiency.

Our endpoint allows our clients to search through huge botanical, fungi or insect database by latin name or common names to get various species info

As the world of houseplants and ornamental varieties continues to flourish, many people now seek more than species identification. Our new 'Varieties' model responds to this demand and offers you a unique opportunity to gain a competitive advantage by enabling the identification of almost 1,000 plant varieties.

We didn't really plan on blogging about yet another Plant.id model upgrade. But we can't help ourselves and have to share the great results. In addition to increasing the number of classes from 33,325 to 35,756, we are excited that performance has improved significantly in Europe and North America.

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.

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.

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.