Verification of color vegetation indices for automated crop imaging applications

By George E. Meyer, J. C. Neto. 2008

988 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5aa233cea2b0ef25d60f3c8826163c83147a6aa6

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In Ecology

In Plant Science

Citations in the corpus

Yield prediction in apple orchards based on image processing (2011, background): “image-based plant indices (Meyer and Neto 2008) in precision agriculture technology applications.”
Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover (2015, background): “…have a value near 1 (Paruelo et al., 2000) to classify pixels that are predominantly in the green band (~500–570 nm), and P3 is a parameter that sets the minimum excess green index, which typically has a value around 20 to select green vegetation (Meyer and Neto, 2008; Richardson et al., 2007).”
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, methodology): “A preliminary calculation of Excess Green (ExG) and Excess Red 223 (ExR) index was followed by subtracting the former from the later, finally gathering the ExG-ExR index 224 (Meyer and Neto, 2008), 225 ExG= 2g − r − b, ExR= 1.”
Tracking the structural and functional development of a perennial pepperweed (Lepidium latifolium L.) infestation using a multi-year archive of webcam imagery and eddy covariance measurements (2011, background): “Surface roughness length is the height above the ground surface where mean wind speed extrapolates to zero (Monteith and Unsworth, 1990), and it governs the rate that momentum is transferred to the land surface.”
Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs (2017, methodology): “In detail, we calculate four different vegetation indices which are often used for the vegetation segmentation, see [14], [18]: Excess Green (ExG), Excess Red (ExR), Color Index of Vegetation Extraction (CIVE), and Normalized Difference Index (NDI).”
Digital repeat photography for phenological research in forest ecosystems (2012, background): “Based on manually taken nadir images of individual plants, the goal of many of these studies was improved distinction between green plants and soil/residue background prior to binarization (Meyer and Neto, 2008; Perez et al., 2000; Woebbecke et al., 1995).”