Input Validator
Match field operation inputs with an external database.
A simple and accurate way to validate product names and active ingredients for standardization and more accurate use of field operations data.
Documentation
Get more out of field operations data by matching user entered product names with structured input data
Product label information
Manually-entered, inconsistent product data typed into the tractor monitor rarely matches the product names in databases like CDMS, Agrian, or Greenbook. Typos, internal product codes, and even just very long product names result in product names that cannot be matched to agricultural product label information like active ingredients, where it is legal to apply the product, and how much of the product should be applied. This information is key for traceability, compliance, insurance, agronomy, carbon, and other use cases. Previously, the matching between manually entered product names in the machine data files and standardized product IDs in product databases was done manually or simply ignored. Leaf solves this problem with an algorithm that matches a product from an operation with a product in an input database such as CDMS or Agrian.
Creating matches
Leaf Input Validator helps you match any product name from a machine file or field operation with a product in an input database. This will return a JSON with matching product information such as product name and productID, the products will be assigned a status: Predicted (suggested by Leaf’s algorithm), Validated (confirmed by user), Pending (no match found), or Invalid, and a score of the match, based on the matching algorithm. You can configure products according to your preferences to choose the database used, match rate accepted (minimum score) and the options given to your users (what they are able to accept and use).
Using standardized data
Many agtech providers such as Farm Management Systems, Agronomic Decision Tools and label certification providers use an external input database to standardize their crop input lists. Leaf Input Validator enables you to match incoming product data with an external input database, allowing you to turn unstructured information into standardized data without having to develop your own matching system and comparison algorithm. This enables you and your grower clients to further use this product data for a wide variety of analyses, and increases your ability to rapidly develop new digital products and services.
“We can leverage the Leaf API to do the heavy lifting of acquiring, processing, and storing data from different sources, and Traction can then take the finished product to add additional value.”
Aaron Hunt, Chief Technology Officer at Traction Ag
Learn moreMatching Products
Standardizing data to create more value
No more unstructured data
User-typed product data can be unstructured, and therefore holds less value for analyses and other purposes. Leaf Input Validator fixes this problem, as long as some similarity is detected, and enables this same data to be used for many new purposes.
Increase the use of operations data
Many growers under-utilize their operations data once it’s captured, as it cannot be connected to their farm records or production data in an unstructured form. Leaf Input Validator solves this problem by turning unstructured data into structured data, giving growers yet another reason to use their operations data for becoming a more data-driven enterprise.
Opening up new opportunities
Agtech providers that work with Leaf are now able to access many new opportunities. From automatically creating product picklists that are tailored to your individual users to introducing new matching criteria to creating custom databases: the opportunities are endless.
Increasing speed-to-market
Speed-to-market is key for any technology provider; spending more time on developing your product and not selling it, means money down the drain and a competitor catching up quickly. Leaf allows agtech companies to focus on building their product and getting it to market as soon as possible, instead of wasting time on building back-end infrastructure.
“We would not have been able to build and maintain the infrastructure ourselves. With Leaf, we have been able to complete integrations faster than we ever thought possible.”
Oliver Wood, Precision Technology Manager of Hutchinsons
Learn moreStart building with machine data
After authentication, Leaf’s API returns a standardized JSON for planting, application, and harvest operations with data summaries, full datasets and rendered maps.
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