Publication detail

Image Processing Based Automated Identification of Late Blight Disease from Leaf Images of Potato Crops

APARAJITA, A. SINGH, A. DUTTA, M. KŘÍŽ, P. ŘÍHA, K.

Original Title

Image Processing Based Automated Identification of Late Blight Disease from Leaf Images of Potato Crops

English Title

Image Processing Based Automated Identification of Late Blight Disease from Leaf Images of Potato Crops

Type

conference paper

Language

en

Original Abstract

Late Blight is one of the most common and devastating disease for potato crops in all over the world. For less use of pesticide and to minimize loss of potato crops, identification of late blight disease is necessary. The conventional method of disease identification is based on visual assessments which is a time consuming process and involves manpower. The proposed work presents image processing based automated identification of late blight disease from leaf images. In the proposed method, adaptive thresholding is used for segmentation of disease affected area from leaf image. The threshold value is calculated using statistical features of image which makes the proposed system fully automatic and invariant under environmental conditions. The proposed method is tested on leaf images of potato crops obtained from plant village database associated with Land Grant Universities in the USA and achieved 96% accuracy. The experimental results indicate that proposed method for segmentation of disease affected area from leaf image is convincing and computationally cheap.

English abstract

Late Blight is one of the most common and devastating disease for potato crops in all over the world. For less use of pesticide and to minimize loss of potato crops, identification of late blight disease is necessary. The conventional method of disease identification is based on visual assessments which is a time consuming process and involves manpower. The proposed work presents image processing based automated identification of late blight disease from leaf images. In the proposed method, adaptive thresholding is used for segmentation of disease affected area from leaf image. The threshold value is calculated using statistical features of image which makes the proposed system fully automatic and invariant under environmental conditions. The proposed method is tested on leaf images of potato crops obtained from plant village database associated with Land Grant Universities in the USA and achieved 96% accuracy. The experimental results indicate that proposed method for segmentation of disease affected area from leaf image is convincing and computationally cheap.

Keywords

Late blight; Leaf; Image processing; Segmentation; Adaptive Thresholding

Released

05.07.2017

Location

Barcelona, Španělsko

ISBN

978-1-5090-3981-4

Book

Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP 2017)

Pages from

758

Pages to

762

Pages count

5

BibTex


@inproceedings{BUT138284,
  author="Aparajita {Aparajita} and Anushikha {Singh} and Malay Kishore {Dutta} and Petr {Kříž} and Kamil {Říha}",
  title="Image Processing Based Automated Identification of Late Blight Disease from Leaf Images of Potato Crops",
  annote="Late Blight is one of the most common and devastating disease for potato crops in all over the world. For less use of pesticide and to minimize loss of potato crops, identification of late blight disease is necessary. The conventional method of disease identification is based on visual assessments which is a time consuming process and involves manpower. The proposed work presents image processing based automated identification of late blight disease from leaf images. In the proposed method, adaptive thresholding is used for segmentation of disease affected area from leaf image. The threshold value is calculated using statistical features of image which makes the proposed system fully automatic and invariant under environmental conditions. The proposed method is tested on leaf images of potato crops obtained from plant village database associated with Land Grant Universities in the USA and achieved 96% accuracy. The experimental results indicate that proposed method for segmentation of disease affected area from leaf image is convincing and computationally cheap.",
  booktitle="Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP 2017)",
  chapter="138284",
  howpublished="electronic, physical medium",
  year="2017",
  month="july",
  pages="758--762",
  type="conference paper"
}