This is like a smart camera system for farms: you point a phone or field camera at plants, and the AI first figures out what part of the plant it’s seeing (leaf, fruit, stem, etc.) and then identifies whether there’s a disease and which one, following a step‑by‑step hierarchy instead of one big guess.
Manual plant disease diagnosis is slow, requires expert agronomists, and doesn’t scale across large fields. This framework automates diagnosis directly from images, enabling faster, more consistent detection of crop diseases in real‑world field conditions.
Specialized hierarchical detection and recognition pipeline tuned for plant structures and diseases, which can be strengthened further with proprietary labeled field imagery from specific crops/regions.
Fine-Tuned
Unknown
High (Custom Models/Infra)
Training and maintaining accurate models across many crops, disease types, and varying lighting/field conditions; on-device inference constraints if deployed on low-power edge hardware.
Early Adopters
Uses a hierarchical object detection and recognition workflow tailored to practical field diagnosis (detect plant/plant parts first, then classify disease), which is more robust to cluttered backgrounds and variable imagery than flat, single-stage disease classifiers.