2020
DOI: 10.34133/2020/3521852
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Abstract: The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differen… Show more

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Cited by 143 publications
(81 citation statements)
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“…In addition to evaluation on the COCO dataset, we further corroborate the proposed method on the Global Wheat Head Detection (GWHD) dataset [55]. e public dataset brings about a challenging task for detecting wheat head from several countries around the world at different growth stages with a wide range of genotypes.…”
Section: Generalization On Global Wheat Head Detectionmentioning
confidence: 57%
“…In addition to evaluation on the COCO dataset, we further corroborate the proposed method on the Global Wheat Head Detection (GWHD) dataset [55]. e public dataset brings about a challenging task for detecting wheat head from several countries around the world at different growth stages with a wide range of genotypes.…”
Section: Generalization On Global Wheat Head Detectionmentioning
confidence: 57%
“…The dataset applied in this paper is the Global Wheat Head Detection dataset GWHD [ 14 ]. The GWHD dataset was constructed collaboratively by numerous countries.…”
Section: Methodsmentioning
confidence: 99%
“…Meanwhile, the CSPNet is used to intergrade the multilevel features. In addition, the latest global wheat-head datasets GWHD [ 14 ] are employed to train the proposed method, as shown in Figure 1 . The proposed method can detect wheat heads quickly and accurately, and also has a good ability of generalization.…”
Section: Introductionmentioning
confidence: 99%
“…A visualization of the process of generating the ground-truth density maps with and without a geometry-adaptive kernel. The top row shows the process on a image of the MINNEAPPLE [25] dataset and the second row an image from the GWHD dataset [24]. From left to right, the raw images with the bounding boxes and pixel annotations, density maps made with a non geometry-adaptive kernel, density maps made with a geometry-adaptive kernel.…”
Section: Parametersmentioning
confidence: 99%