Cotton Leaf Disease Detection Using Texture and Gradient Features
A.Sabah Afroze1, M. Parisa Beham2, R. Tamilselvi3, S.M. Seeni Mohamed Aliar Maraikkayar4, K.Rajakumar5
1A.Sabah Afroze, Department of ECE, Sethu Institute of Technology, Kariapatti, Virudhunagar, (Tamil Nadu), India.
2M. Parisa Beham, Department of ECE, Sethu Institute of Technology, Kariapatti, Virudhunagar, (Tamil Nadu), India.
3R. Tamilselvi, Department of ECE, Sethu Institute of Technology, Kariapatti, Virudhunagar, (Tamil Nadu), India.
4S.M. Seeni Mohamed Aliar Maraikkayar, Department of ECE, Sethu Institute of Technology, Kariapatti, Virudhunagar, (Tamil Nadu), India.
5K.Rajakumar, Department of ECE, Sethu Institute of Technology, Kariapatti, Virudhunagar, (Tamil Nadu), India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 700-703 | Volume-9 Issue-1, October 2019 | Retrieval Number: F9083088619/2019©BEIESP | DOI: 10.35940/ijeat.F9083.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The detection of cotton leaf disease is a very important factor to prevent serious outbreak. Most cotton diseases are caused by fungi, bacteria, and insects. A new method is proposed for careful detection of diseases and timely handling to prevent the crops from heavy losses. A disease due to bacteria, insects and fungus occurs in the cotton leaves in the range of about 80-95%. In the proposed work, first the group of infected leaves and normal leaves are collected and the image preprocessing is done using Adaptive histogram equalization for enhancing the contrast. In feature extraction phase, texture and gradient feature are extracted using Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG) and Differential of Gaussian (DOG). K- Nearest neighbor classifier is applied to classify the leaf image as a unaffected or an affected leaf. A cotton leaf database is internally created to evaluate the efficacy of our algorithm. The validate results show that the proposed method achieved higher classification accuracy in lower computational time.
Keywords: Cotton leaf diseases, Adaptive histogram, Texture feature, Gradient feature, KNN classifier.