Earticle

초록

영어

Soybean is the main food crop and an important economical crop of the world. Proper disease control measures must be undertaken to minimize losses. Techniques of machine vision and image processing were applied mostly to plant protection in recent years. Disease detection and segmentation are very important, but the diseases of soybean are complex in real environment and traditional segmentation methods cannot quickly and accurately obtain segmentation results. This research presented a new method for soybean leaf disease detection based on salient regions. This method used low-level features of luminance and color, combined with multi-scale analysis to determine saliency maps in images, and then K-means algorithm was used. The experimental results show that this method can accurately extract the disease regions from soybean disease leaf images with complex background, and it can provide an excellent foundation for extracting disease feature and identifying the diseases categories.

목차

Abstract
 1. Introduction
 2. Principle of Itti Method
 3. Soybean Leaf Disease Extraction Based on Salient Regions
  3.1 Saliency Map of Soybean Disease Image
  3.2 Diseases Segmentation Using Saliency Maps
  3.3 Correction for the Diseases Extraction
 4. Experiments and Results
 5. Conclusions
 Acknowledgment
 References

키워드

저자

• Jiangsheng Gui [ College of Information, Zhejiang SCI-Tech University, Hangzhou, China ]
• Li Hao [ College of Information, Zhejiang SCI-Tech University, Hangzhou, China ]
• Qing Zhang [ College of Information, Zhejiang SCI-Tech University, Hangzhou, China ]
• Xiaoan Bao [ College of Information, Zhejiang SCI-Tech University, Hangzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605