Counting Error before Counting Accuracy: A Reproducibility Audit of Sorghum Panicle Metrics

Abstract

Automated panicle counting can make field phenotyping more scalable, but its evaluation depends on a traceable relationship between predictions and reported error measures. This article performs a secondary arithmetic audit of the ten rows in a published sorghum counting table. All ten reported root mean squared errors are smaller than their corresponding mean absolute errors. That ordering is incompatible with the standard definitions if the measures use identical scalar errors, weights, and units. The reported root mean squared errors also lie within 0.054 of the square roots of the reported mean absolute errors, an exploratory numerical pattern that does not identify the original computation. Without raw predictions, no corrected performance estimate can be recovered. The article then develops a field-oriented evaluation design addressing spatial splitting, object formation, density-dependent error, and annotation uncertainty. It reports no new image processing or network training. The contribution is a reproducible audit and a specification of the evidence needed to interpret counting performance. Keywords: sorghum; panicle counting; UAV imagery; metric consistency; spatial validation; phenotyping.