SHAPE AND SIZE ANALYSIS OF CORN PLANT CANOPIES FOR PLANT POPULATION AND SPACING SENSING

Abstract

An effective corn plant population and spacing sensing system may provide a key layer of field variabilityinformation useful for crop management. An algorithm was developed to count corn plants and to estimate plant location andintra-row spacing in segmented images of 6.1-m (20-ft) long row sections. Images were scanned to detect and determine theboundaries of top projected corn plant canopy objects using a chain code methodology. Plant objects were fused togetherbased on a multi-step process that took into account the spatial structure of the crop row. Position, roundness, and area ofplant canopies were used to distinguish between corn plants and weeds. Estimates of plant counts in row sections werecompared with manual counts across three growth stages, three populations, and three tillage treatments. Overall, the systemestimated the number of plants with an RMSE of 1.49 plants per row section, which corresponds to 6.2% RMSE or 3210plants/ha (1300 plants/acre). No evidence of significant differences in mean plant spacing estimates was detected althoughsignificant, albeit small, increases in spacing variance were detected. These results demonstrate the importance of canopyshape and size analysis in the implementation of a machine vision plant population and intra-row spacing sensing system.