Detail publikace

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.

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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",
  doi="10.1109/TSP.2017.8076090",
  howpublished="electronic, physical medium",
  year="2017",
  month="july",
  pages="758--762",
  type="conference paper"
}