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Category
Enterprise AI
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Stack
Industrial Intelligence
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Timeline
5 Months
// problem
A leading agri-tech company encountered significant challenges in early disease detection across expansive farmlands. Manual crop inspections proved inconsistent, labor-intensive, and reactive. With hundreds of acres under cultivation and a wide variety of crops, their agronomy teams struggled to scale disease monitoring efforts. This resulted in delayed diagnoses, reduced crop yields, overuse of pesticides, and an increased reliance on guesswork rather than actionable, data-driven insights.
// How we fixed it
We developed AgriAid, an AI-powered crop disease analysis platform that allows farmers and field officers to capture or upload crop images using mobile devices. Within seconds, the platform identifies potential diseases, recommends precise treatments—including fertilizers and pesticides—and generates comprehensive health reports. The system works offline and syncs with cloud dashboards when internet is available.
// How we made it happen
We partnered closely with the client’s agronomists to gather disease data across multiple regions and crops. Using this, we trained a multi-class convolutional neural network (CNN) to detect 35+ common and region-specific crop diseases with high accuracy. The platform was built using TensorFlow Lite for edge inference, React Native for cross-platform mobile access, and Firebase for report generation and user management. The recommendation engine was trained with the client’s treatment protocols, enabling it to suggest stage-specific remedies and preventive actions.
+97%
Accuracy in detecting
+40%
Yield Increase
<5s
Diagnosis Time delivered
