Study of Recognition Method of leguminous Weed Seeds Image

Shi, Changjiang; Ji, Guangrong

doi:10.1109/iwisa.2009.5073126

Cited by 4 publications

(1 citation statement)

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“…The recognition rates reached 99.3% and 98.2% tested by a large scale seed base containing 10,310 images of 236 different weed species with color images and black and white seed images, respectively. Shi et al [6] proposed a seed identification system for leguminous weeds. In their work, the feature vector composed of 16 components includes the shape geometry and the inner structures of the seeds and umbilicus.…”

Section: Introductionmentioning

confidence: 99%

Seed Identification of Gramineous Grass Based on Difference of Local Fractal Dimensions

Pan¹,

Chen²,

Ma³

et al. 2016

Open Cyber

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This work proposes an approach based on the difference of local fractal dimension (DLFD) for seed identification of gramineous grass, rather than shape and color of the seeds. Being an important forge category of grassland, gramineous grass has been rarely investigated for the automated identification task by the researchers. Three main steps are involved in the extraction of DLFD. At first, the ROI image is equally divided into local blocks, and the fractal dimension of the partitions are calculated. Based on the average fractal dimension of all the blocks, the DLFD can then be obtained by subtracting the individual fractal dimension and the average, magnifying the contrast of the self-similarity of the images. Euclidean Distance and the nearest neighbor classifier are finally used for similarity measurement and classification. The novelty of the approach lies in applying fractal geometry in forage seed identification, a quite new area for pattern recognition. The experimental results demonstrate the effectiveness of the proposed method by some comparative analysis.

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Section: Introductionmentioning

confidence: 99%