Pest identification from the damage, not just the insect
Pests are often gone by the time you look. Agrofuze reads the damage they leave behind - the pattern of holes, trails and residue - to flag the likely culprit species from a photo.
Damage patterns the model recognizes
Each pattern points toward a category of pest, which narrows the likely species list.
Chewing damage
Ragged holes and eaten leaf margins left by caterpillars and beetles.
Skeletonizing
Leaf tissue stripped down to the veins, a signature of certain larvae.
Stippling and specks
Fine pale flecking from sap-feeding mites and thrips.
Mining trails
Winding pale tunnels left inside the leaf by leaf miners.
Sooty residue
Dark mold on honeydew, an indirect sign of aphids or whitefly.
Boring entry
Puncture points and frass where borers enter stems or fruit.
Where pest coverage stands today
Pest identification is the fastest-moving part of the engine, and the honest position matters more than an inflated one.
- Damage-pattern recognition is stronger than exact species naming, so the culprit is given as a likely category first.
- Regional species coverage is still expanding - a low-confidence species read is shown as exactly that.
- When damage is ambiguous, the engine says so rather than forcing a name.
Not sure if it is pests or disease?
The engine weighs disease, pest and deficiency signals in the same pass, so look-alikes get separated.
Nutrient deficiency analysis