Structure input data consistently with Leaf's new 'Input Validator'

12 Dec, 2023

Structure input data consistently with Leaf's new 'Input Validator'

Structure input data consistently with Leaf's new 'Input Validator'

Accurate seed and chemical names are critically important for traceability, chemical compliance, carbon credits, farm management, crop insurance and many other use cases. Today, input data is typically entered manually and freely, which leads to many inconsistencies like typos, internal codes, and overly long product names. This makes it difficult to match inputs with information like active ingredients, where it is legal to apply the product, and how much of the product should be applied. Previously, technology providers had to match these product names with standardized IDs in databases manually, or overlooked this process altogether.

Today we are excited to introduce a new, automated way to match inputs from field operations to input databases. Introducing Leaf Input Validator, now available for inputs used in the United States.

Input Validator helps solve this problem in two ways.

  • From the start: a type to search API that links to a product list, cross associated with the leading product databases in the industry. This allows correct input names to be entered before an operation happens.

  • Retroactively: For historical data, or when input names are manually entered, Leaf’s validation model matches the input used with known inputs sourced from leading input databases. Associations can be created, removed, and updated on an account or user level, which gives users a customized experience, and Leaf’s validation model uses these adjustments to further improve predictions.

Input matching features

Leaf's Input Validator has two main features:

  • Solve the problem from the start with a standardized input list. Leaf's Input Validator includes a type-to-search endpoint that will display all relevant product names to a user as they type. If a farmer or agronomist is planning a field operation, they can now easily select known products and send them to the machines using Leaf’s Prescriptions service.
  • Handle input name errors retroactively in already collected data. Not all data (and certainly not historical data!) will include known input names. Leaf Input Validator automatically identifies unknown inputs from field operations, predicts the most likely match across all available input databases like CDMS and Agrian, and returns the results so that companies can programmatically match and structure this input information consistently. The matching also gets better over time. Users can associate product names from field operations with known products at an account or user level and build a private library of known product name associations.

How does Leaf's Input Validator work?

Leaf's Input Validator takes inputs from field operations and compares the product names against external databases, CDMS and Agrian (as well as John Deere). Leaf’s comparison algorithm then assigns a ‘match score’, indicating the likelihood of a successful product match. The higher the score, the more confident you can be in the accuracy of the match.

As part of the validation process, you can customize score limits and choose to override suggested matches. Products are also assigned a status: Predicted (suggested by Leaf’s algorithm), Validated (confirmed by the user), Pending (no match found), or Invalid.

Once a product matches, you’ll be able to access the following properties:

  • Name
  • Active ingredient
  • Labels
  • Registration
  • Distributor
  • Registrant
  • Product type
  • Formulation type
  • Label provider
  • Product page URL

In order to get the full product label information, you will need to subscribe to external databases like CDMS and Agrian. We will be happy to connect you with them!

How to get started

Ready to unlock a range of new data-driven insights for your customers? Reach out to via email to find out more. We’d love to hear from you and learn more about what you are looking to build. If you’re interested in the documentation, take a look in more detail here.

P.s. Yes Leaf's product matching tool is AI-powered, but we thought we'd spare you the buzzword. Be sure to let us know if you have any questions.

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 Leaf | Data infrastructure for agriculture

G. Bailey Stockdale

CEO and Co-Founder

G. Bailey Stockdale, originally from California, Co-Founded Leaf Agriculture in May 2018 and is currently serving as the CEO. His previous experience with SP Ventures as an Entrepreneur in Residence (located in the São Paulo Area of Brazil) as well as founding one of the first large-scale agriculture remote sensing companies in Brazil fostered the experience and connections leading to the foundation of Leaf. Bailey has extensive knowledge of agriculture atomization and is passionate about making it easier to build with farm data.

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