AI Crop Intelligence
Disease detection, yield prediction, prescription generation — trained on your historical data, owned by you.
The problem we solve.
Off-the-shelf AgriTech AI is trained on someone else's data and runs in someone else's cloud. The models drift on your crop, your geography, your varieties. The 'insights' you get back are commodity.
JMJ builds custom AI for enterprise customers who need their models to actually fit their operation — trained on the customer's own historical data, deployed in the customer's own environment, and explainable enough that an agronomist can defend every recommendation.
Engineered, not improvised.
Custom computer-vision models for crop scouting (disease, pest, weed, nutrient deficiency)
Yield prediction models combining historical yields + weather + soil + remote sensing
Prescription-generation models that output variable-rate maps directly into your equipment
Explainable AI (XAI) layer so every prediction has a defensible rationale
On-prem or private-VPC deployment — your models, your weights, your IP
What you should expect.
Engineering specifics.
We are vendor-agnostic where possible and opinionated where it matters. The stack above represents typical components; final selections depend on your operating environment, budget, and compliance posture.
Built to federal-grade standards.
Models are trained on your data, weights are stored in your environment, and inference happens on-prem or in your VPC. JMJ does not retain training data, model weights, or inference logs beyond the engagement. Aligned with NIST AI RMF and emerging USDA AI standards.