Agtech Model Training for Smarter, More Sustainable Farming

Challenge

Our Agtech client relied on visible signs to spot plant diseases, due to which, yields were lost, and treatments were less effective. Their crop protection practices sprayed entire fields, wasting resources, increasing costs, and harming the environment. A smarter solution was needed to detect problems early, before symptoms appeared, and to use robotics and precision spraying to intervene only where it was truly needed.

DDD’s Solution

At Digital Divide Data (DDD), we produced high-quality annotated images across diverse crops, marking diseased areas and tiny insects that threatened plant health. Each annotation became vital training data that helped AI detect issues earlier and guide smarter interventions. Our HITL-based Agtech model training allowed spotting of crops before symptoms were visible, guided robots to only affected areas, reduced chemical costs, mapped harmful insects, and enabled stronger, more scalable agtech models. 

“Driving higher yields with smarter and sustainable farming solutions.”

Impact

The work we did for this customer goes beyond annotation; it laid the foundation for a smarter, more resilient agricultural ecosystem. By building precise, large-scale datasets, we made it possible to:
● Auto-annotate crop images with accuracy, reducing the reliance on manual labor.
● Lower operational costs by automating scouting and monitoring tasks.
● Apply crop protection with pinpoint precision, minimizing costs and environmental impact.
● Adapt solutions across different crops and regions, making the technology globally relevant.
● Protect food security by detecting problems earlier and reducing both yield losses and post-harvest waste.