Implement data collection procedures.

I have the draft data-collection template and the list of field teams for the crop health survey. Implement the collection sheet so each team logs date, GPS coordinate, observed pest, severity on a 1–5 scale, photo filename, and analyst initials; validate required fields and drop any rows missing GPS or date before I issue it to field teams on Monday.

Before I distribute the data collection sheet to field teams, make it easy to use and to audit later: require GPS and photo filename fields, place the most commonly missed fields at the top, add a one-line tooltip on how to measure severity 1–5, and flag rows with improbable coordinates or duplicate photo filenames that will make postcollection validation slow.

We got back the first batch and teams used different interpretations of severity and renamed photos arbitrarily; the tech lead is out and I can't re-survey. Should I standardize severity into three broader categories now and rename photos to a consistent pattern, or go back to teams for clarification and risk missing the analysis deadline? Which option preserves data integrity best?

After every survey I spend two days cleaning GPS errors, standardizing photo names, and mapping severity scales before analysis. That delays insights to agronomy and costs credibility. What changes to the collection template, training, or a simple validation routine will cut that cleanup time in half and catch the usual errors before teams leave the field?