Plant.id Health Assessment

Plant.id Health Assessment – plant diseases identification API

Identify pests, plant pathogens, nutrient deficiencies, and more with our plant diseases identification API.

Plant.id Health Assessment provides information about the overall health of the plant (is_healthy binary classification), diagnostics of a specific problem (diseases classification), and information about the disease.

With Plant.id Health Assessment you can recognize:

  • 39 classes of fungal diseases
  • 15 classes of abiotic disorders
  • 14 classes of pests
  • 9 classes of chromista
  • 7 classes of viruses
  • 6 classes of bacterial diseases

Model specification

100 diseases classes

We can identify more specific issues (such as thrips and nutrient deficiencies) and generic ones (such as abiotic and fungi). The plant diseases identification API has been trained mostly on houseplants.

47.9% accuracy

The precision of the binary is_healthy classification is 81.3%, the recall is 83.1%. The model for 100 classes returns an exact match in 47.9% of cases, the TOP3 accuracy is 61.3%.

{
   "is_healthy_probability":0.001715570500000041,
   "is_healthy":false,
   "diseases":[
           {
         "classification":[
            "Abiotic",
            "Water-related issue"
         ],
         "name":"Water excess and/or uneven watering",
         "probability":0.910850346
      },
      {
         "classification":[
            "Abiotic",
            "Water-related issue"
         ],
         "name":"Root rot",
         "probability":0.1706690415
      },
   ],
   "diseases_simple":[
      {
         "name":"Abiotic",
         "probability":0.9738712015
      }
   ]
}

Pricing

The base price is €0.1 per request. There are significant discounts for higher volumes of identifications. Tell us what you need on business@plant.id.

We provide prepaid and retroactive models. You can pay with a card, wire transfer, or PayPal.

Future of diseases identification API

We are continually feeding the model with fresh photos from our annotation pipeline and will be releasing new models on a monthly basis in 2021. We are planning the following improvements.

2021

  • Adding new categories and reviewing the existing ones.
  • Adding treatment instructions, common names and description.

2022

  • Improvements in accuracy: we will be adding new annotations to improve the results.
  • Similar images of diseases: we would like to achieve better explainability of the result.
  • Symptom recognition: we would like to show a list of both possible and identified symptoms.
  • Disease seriousness estimation.