Contact person: Business Client

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Location: Portland, United States

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I need a locally hosted AI workflow that pulls live irrigation data from the Fieldnet Nextgen API, compares it with our planned schedule stored in Google Sheets, and then surfaces the gaps on a web-based dashboard. The model can run on-premise (Linux server or Windows box—whichever you are most comfortable provisioning) and should crunch discrepancies in near-real-time.

For the first milestone, I want:

• A lightweight AI or rules-based model that ingests Fieldnet JSON, grabs the relevant Google Sheets range via API, and flags any “off-schedule” pivots, zones, or runtimes.
• A responsive web dashboard (React, Vue, or plain Django templates—your call) that shows those results as clear graphs/charts plus sortable tables.
• User authentication tied to our farm’s existing Google Workspace so staff see only the fields that matter to them.
• Clean documentation and deployment scripts so the whole stack can run offline on our farm network after hand-off.

Future-facing: once the web piece is solid I’ll look at repackaging some views into an iOS companion app, so structuring the code with a REST or GraphQL layer will help.

Acceptance criteria
– Fieldnet API calls succeed with our keys and respect rate limits.
– Google Sheets data syncs on a schedule we can tweak (cron or similar).
– Dashboard loads in <2 s on local Wi-Fi and displays both chart and table views with identical underlying numbers.
– Clear README lets a non-developer on the farm redeploy from scratch.

If you’ve wrestled with agricultural IoT data, TensorFlow/PyTorch, or modern JS dashboards, your experience will shine here. Shoot me a note outlining how you’d architect the data flow, and we can get moving." (client-provided description)