Precision Agriculture Series: Data Challenge #1- Workforce Management

This is the second post in our “Precision Agriculture series.” In this part, we’ll address the first challenge of data labeling: workforce management. Be sure to stay tuned for our second post addressing the second challenge in precision agriculture: dataset quality

Agritech companies have to perform a tricky balancing act between expanding their workforce quickly, efficiently and accurately, while still managing a large and diverse team. At Dataloop, we’ve seen successful startup teams and enterprises begin by managing data labeling and other data processing needs in-house. This works, but only as long as datasets remain a manageable size. Data labeling is a high volume task, and quality matters just as much as quantity

As enterprises grow, the whole operation and the need for data management becomes crucial.

Workforce Management for Agritech Solutions

Managing a streamlined, effective data labeling workforce is one of the biggest obstacles to successful data labeling because it encompasses two serious pain points:

  1. Hiring and retaining a large enough workforce to keep up with the enormous flood of unstructured data that arrives continually.
  2. Ensuring that a varied and large group of workers can deliver consistently high-quality data.

To provide protection against diseases and pests gaining a foothold in your crops, you’ll need to train ML pattern recognition models on data that consistently and accurately identifies the earliest signs of infestation or disease. This way you can act quickly to stop your crops from being irrevocably damaged. 

ML models detecting diseased leaves

You want your ML models to be able to distinguish instantly between healthy leaves or produce, and diseased or infested leaves or produce, so you need enough high quality data for you to effectively train your models in order to determine how to recognize the signs of disease or infestation– just by looking at hi-resolution images from a drone. That in turn means you need data labelers who know all the different species in your fields, so they can spot diseased or infested leaves and classify them correctly. 

Data Management for ML/AI Data

Efficient data labeling demands both speed and quality in equal measures. This leaves Agritech companies to find the right balance between swiftly scaling up their data labeling teams so that they can handle the volume of data, and moving slowly enough to ensure that each worker gets the necessary oversight and training to label data accurately. 

It’s common for startups and enterprises alike to start off with in-house data labeling and data processing, but we’ve repeatedly seen that as your farming business expands and your datasets increase in unison, the workload becomes too much to handle in-house. When they reach this point, many companies turn to external solutions that will allow them to scale while increasing data quality and workforce efficiency, and productivity. 

💡 It is important to keep in mind that taking the external solution route is a more costly and complex process that entails working with multiple vendors. 

ML models are able to utilize human-in-the-loop annotated data that’s derived from hi-resolution drone images in order to distinguish between healthy, and altered produce. Data labelers are the essential piece in identifying the various species of farm products, and understanding on a granular level their anatomies in order to decipher, and classify their attributes and characteristics.

This solves the issue of finding enough workers, but opens up new challenges:

  • Training a sizable number of labelers to carry out assigned tasks.
  • Seamlessly assigning work across large, varied teams and dividing tasks up into individual assignments.
  • Monitoring individual progress without losing track of the project as a whole.
  • Enabling frictionless communication and collaboration between labelers and data scientist(s) to maintain quality control, validate data, and resolve workforce issues.
  • Overcoming language, geographic, and cultural barriers between labelers who might fail to annotate data correctly because they’ll miss certain cultural cues.

It is important to keep in mind that just generating data is not enough, ensuring its quality is also important in order to produce high-quality training data for deep learning models.

Further reading: How Enterprises Can Extract Meaningful Insights from Unstructured Data

Is managing your workforce taking up too much of your time? Or, are you frustrated at how long it’s taking to annotate your data? Or, maybe you’re just fed up that the quality just isn’t good enough? If anything I’ve mentioned resonated with you, and you’d like to speak to an expert and learn more, then click here

Be sure to stay tuned for our third post addressing the second challenge in precision agriculture: Dataset quality.

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