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- W3037772610 abstract "The quantification and identification of ground cover plays a key role in erosion modelling, weed measurement, plant disease identification and other environmental applications. Currently, a variety of methods are used to mechanically classify digital images for ground cover. Only a few of these methods can distinguish green vegetation, straw/dormant vegetation, and exposed soil using only the Red-Green-Blue (RGB) spectrum. This research presents an approach to classifying ground cover using standard JPEG images and readily available Matlab (2018b) functions. The approach uses block segmentation, as opposed to pixel-wise or object-based segmentation, and compares multiple machine learning approaches with varying pixel block size and classification acceptance thresholds. The most successful classification approach found through this study was the decision tree algorithm with a 70-pixel block size and 60% classification acceptance threshold. Images were reduced to three feature descriptors: colour, texture, and oriented gradients to represent the respective RGB spectrum for an image. Both the training set and test set images used in this research came from field and greenhouse studies done between 2016 and 2019. The produced classifications were compared to manual coverage classifications using Samplepoint, a grid-based method, with R-squared values of 0.86 for green vegetation, 0.87 for straw/dormant vegetation, and 0.96 for exposed soil, respectively. This method showed strong performance for images containing exposed soil and either green vegetation or straw/dormant vegetation. The method was less effective for images with large quantities of both green vegetation and straw/dormant vegetation likely due to their similar shape." @default.
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- W3037772610 date "2020-08-01" @default.
- W3037772610 modified "2023-10-16" @default.
- W3037772610 title "Measuring soil coverage using image feature descriptors and the decision tree learning algorithm" @default.
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- W3037772610 doi "https://doi.org/10.1016/j.biosystemseng.2020.06.002" @default.
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