Reduced-reference Crop Image Quality Assessment Based on Random Gabor Feature

doi:10.3969/j.issn.1006-2475.2020.05.012

Abstract

Abstract: With the deepening reform of agricultural modernization, informatization and automation, information technology plays an important role in its development process, and gradually serves all aspects of agricultural production. Among them, the new intelligent agricultural system based on computer vision and digital image processing technology has become the research focus and hotspot in the agricultural informatization development. For this reason, this paper takes crop image as the research object, and proposes a reduced-reference crop image quality assessment model based on random Gabor feature. The kernel function of Gabor filter can better describe the field characteristics of simple visual neurons. Its multi-frequency and direction characteristics are similar to the way of humans visual system which perceives images. Based on this fact, Gabor filter is used to analyze the texture and edge distribution characteristics of crop images, and a reduced-reference quality assessment model is established. The experimental results show that the crop image quality assessment model proposed in this paper can recognize and perceive the degradation of crop image quality very well, and plays an important role in the new intelligent agricultural system.

Key words: intelligent agriculture, computer vision, Gabor feature, reduced-reference quality assessment

CLC Number:

TP391

WU Shi-hai, BAO Yi-dong, CHEN Guo, CHEN Qiu-shi. Reduced-reference Crop Image Quality Assessment Based on Random Gabor Feature[J]. Computer and Modernization, 2020, 0(05): 70-.

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