MACHINE-VISION WEED DENSITY ESTIMATION FOR REAL-TIME, OUTDOOR LIGHTING CONDITIONS

Abstract

A system to estimate the weed density between two rows of soybeans was developed. An environmentally adaptive segmentation algorithm (EASA) was used to segment the plants from the background of the image. The effect of two image data transformations on the segmentation performance of the EASA was investigated, and the RGB-IV1V2 transformation resulted in significantly higher quality segmentation results based on morphological opening and closing pixel loss over the RGB-rgb transformation. An adaptive scanning algorithm (ASA) was developed and used to automatically detect crop inter-row edges and to estimate the number of weeds in the inter-row area. Two sets of images were acquired under sunny and overcast sky conditions. The ASA-detected crop row edge positions were significantly correlated with the manually detected crop row positions, with the distribution skewed towards positions internal to the row. ASA weed density estimates were highly correlated with manual weed counts for both lighting conditions. However, when a limited range of the data was considered, much lower correlations resulted, revealing a loss of spatial color resolution due to the transmission of the video signal. The mean execution time of the ASA was 0.038 s for 0.91 m (3 ft) long inter-row regions showing that the algorithm met the real-time constraints necessary to be used as a sensing system for a variable-rate herbicide applicator.