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• W2916731097 abstract "Plant architecture characteristics contribute significantly to the microclimate within peanut canopies, affecting weed suppression and incidence and severity of foliar and soil-borne diseases. However, plant canopy architecture is difficult to measure and describe quantitatively. In this study, a ground-based LiDAR sensor was used to sense peanut canopy structure and a data processing and analysis algorithm was developed to extract feature indices to describe the canopy architecture. A data acquisition platform was constructed to carry the ground-based LiDAR and a RGB camera during field tests. An experimental field was established with three peanut cultivars at Oklahoma State University’s Caddo Research Station in Fort Cobb, OK in May and the data collections were conducted monthly from July to September 2015. The ground-based LiDAR used for this research was a line-scan laser scanner with an scan-angle of 100°, an angle resolution of 0.25°, and a scanning speed of 53msec. The collected line-scanned data were processed using the developed image processing algorithm. The canopy height, width, and density were evaluated. Entropy, cluster count and Euler number were extracted from the image, and used to describe the shape of the peanut canopies. The three peanut cultivars were then classified using the shape features and indices. The error of height measurement statics was 18.28 mm. This approach should be useful for phenotyping peanut germplasm for canopy architecture." @default.
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• W2916731097 date "2019-02-28" @default.
• W2916731097 modified "2023-10-15" @default.
• W2916731097 title "Development of a Peanut Canopy Measurement System Using a Ground-Based LiDAR Sensor" @default.
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• W2916731097 doi "https://doi.org/10.3389/fpls.2019.00203" @default.
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