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Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Applied Engineering in Agriculture. 21(2): 295-303. (doi: 10.13031/2013.18144) @2005
Authors:   D. S. Shrestha, B. L. Steward
Keywords:   Precision agriculture, Machine vision, Image processing, Crop canopy, Sensing system, Freeman chain code

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

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