Automated internode length measurement of cotton plants under field conditions

Abstract

An in-field vision system has been developed that automatically and non-destructively measures internode length (i.e., the distance between successive main stem branches) of plants in a growing cotton crop for the purpose of inferring plant growth performance and informing crop irrigation management. The system uses monocular video acquisition behind a plant-contacting transparent panel that moves through the crop. Line features are extracted from acquired imagery to estimate candidate nodes on the plant's main stem via a two-stage process that may be implemented in real-time. Firstly, candidate nodes are identified in each individual image; secondly, the comparison of sequential images permits the removal of erroneously identified nodes (false positives) and compensation for missed nodes (due to occlusion by a leaf, commonly). Ninety-five internode length measurements were automatically detected from 168 video sequences of 14 plants. Algorithm run-time calculations and the rate of internode measurement acquisition were shown to be adequate for real-time input to spatially varied irrigation control. For this data set, the median absolute error was 5.3 mm in comparison with physical plant measurements, and the standard error in measurement ranged from 1.1 to 5.7 mm, with an average of 3.0 mm.