Abstract
The extraction of automated plant phenomics from digital images has advanced in recent years. However, the accuracy of extracted phenomics, especially for individual plants in a field environment, requires improvement. In this paper, a new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed. The algorithm was applied on perennial ryegrass row field data multispectral images taken from the top view. First, the center points of individual plants from digital images were located to exclude plant positions without plants. Second, the accurate area of each plant was extracted using its center point and radius. Third, the accurate mean normalized difference vegetation index of each plant was extracted and adjusted for overlapping plants. The correlation between the extracted individual plant phenomics and fresh weight ranged between 0.63 and 0.75 across four time points. The methods proposed are applicable to other crops where individual plant phenotypes are of interest.
Highlights
Due to the exponentially increasing consumption of food, fuel, and feed by the burgeoning population of the world, global agricultural demand is growing
Measured seasonal fresh biomass weight in 2017 indicated a wide variability of biomass values (~1.41–428 g) for each measurement season for the individual plants [34]. This suggests that biomass yield had sufficient variation to use to correlate the normalized difference vegetation index (NDVI) and plant phenomics
There are two main reasons for this: (1) most plants in these three field trial images had overgrown across bounding boxes and merged with adjacent plants, rectangular bounding boxes will not provide an accurate measure of the area; (2) rectangular boxes may show an area that is entirely due to the overlapping of neighboring plants, leading to an area or NDVI being attributed to missing plants
Summary
Due to the exponentially increasing consumption of food, fuel, and feed by the burgeoning population of the world, global agricultural demand is growing. Let I (i, j) represent the NDVI value of the image pixel at the ith row and jth column of I, and I (i, j) ∈ [−1, 1]. The first step was to remove any background values, which did not contain plant pixels. Grey pixels represent normalized difference vegetation index (NDVI) intensity and black pixels represent shown in Figure 5a–d, respectively. Grey pixels represent normalized difference vegetation index the background. If there was a center point in a bounding box it was determined as containing a plant and vice-versa.
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