AgritechAI

One platform to run the farm on data

Agritech-AI turns field and pond sensor readings into daily decisions: when to irrigate, how much salt to leach, when to run the drains, and how much to feed. Every recommendation names the model that issued it and its data source.

Try the models

The demo runs on simulated data for a saline-soil field and two tilapia ponds in Kafr El-Sheikh, read-only.

From reading to decision

  1. 1

    Measure

    Soil, groundwater, irrigation, drainage and pond sensors, from our devices or our partners'.

  2. 2

    Model

    Established physics (FAO-29, FAO-56, Emerson) as the baseline, then machine-learning models trained on field data.

  3. 3

    Recommend

    A clear decision with a date and an amount, sent to the agronomist and the farmer, with the reason.

What the platform covers

Try the models yourself

Live calculations from the platform's engines. Nothing you enter is stored.

Leaching requirement (A1)

The leaching fraction that keeps soil salinity within the crop's tolerance at 90% yield.

Un-ionized ammonia (C1)

The toxic share of total ammonia at the given pH and temperature.

Daily feed ration (C3)

Feed for tilapia ponds by fish size, temperature and water quality.

The company and the Unit

Agritech-AI is a Kafrelsheikh University spin-off founded by Prof. Dr. Wael Mohamed Elmessery, the inventor and holder of the platform's intellectual property. The AI in Agriculture Unit at the Faculty of Agriculture uses the platform under a free academic licence, and the company supplies technology to graduate startups without holding any equity in them.