Telling deficiency apart from disease - the call people get wrong
A yellowing leaf could be hungry or sick, and the treatments pull in opposite directions. Agrofuze is built to separate the two, because guessing here wastes inputs and lets the real problem run.
How the engine draws the line
Deficiency and disease leave different fingerprints, and the model is trained to weigh them against each other rather than in isolation.
- Distribution - deficiency tends to be uniform and symmetrical; disease is often patchy and irregular.
- Leaf age - which leaves are affected first is a strong tell between nutrients.
- Pattern versus lesion - a smooth color shift reads differently from a defined lesion or spot.
- Combined weighing - disease and deficiency signals are scored together, so the more likely cause wins with its confidence shown.
The shortfalls the engine reads
Each deficiency carries its own visible signature, shown in the leaf it affects.
Nitrogen (N)
Uniform paling and yellowing that starts on the older, lower leaves first.
Potassium (K)
Scorching and browning along leaf margins while the center stays greener.
Phosphorus (P)
Dull, dark tone with purple or reddish tints on older foliage.
Magnesium (Mg)
Yellowing between the veins while the veins themselves stay green.
Iron (Fe)
Interveinal yellowing that shows on the youngest leaves first.
Calcium (Ca)
Distorted, hooked new growth and weak tissue at the leaf tips.
Confirmed the cause. Now what?
Once the engine separates deficiency from disease, it suggests a next step for the leading finding.
Treatment recommendations