Fungal Disease in Cotton Leaf Detection and Classification using Neural Networks and Support Vector Machine
Ch. Usha Kumari1, N. Arun Vignesh2, Asisa Kumar Panigrahy3, L. Ramya4, T. Padma5
1Ch. Usha Kumari, Professor in Department of Engineering and Communication Engineering, GRIET, Hyderabad, Telangana, India.
2N. Arun Vignesh, Associate Professor in Department of Engineering and Communication Engineering, GRIET, Hyderabad, Telangana, India.
3Asisa Kumar Panigrahy, Associate Professor in Department of Engineering and Communication Engineering, GRIET, Hyderabad, Telangana, India.
4L. Ramya, Lecturer in Department of Engineering and Communication Engineering VNRVJIET, Hyderabad Hyderabad, Telangana, India.
5T. Padma, Professor in Department of Engineering and Communication Engineering, GRIET, Hyderabad, Telangana, India.
Manuscript received on 12 August 2019 | Revised Manuscript received on 19 August 2019 | Manuscript published on 30 August 2019 | PP: 3664-3667 | Volume-8 Issue-10, August 2019 | Retrieval Number: J96480881019/2019©BEIESP | DOI: 10.35940/ijitee.J9648.0881019
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© 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: Agriculture productivity is the main factor for improving economic status of India. Reduction in production rate is mainly due to various diseases in plants. Identification of plant disease in early stage is the main challenge for improving the production rate as well as economic status. This paper presents automatic disease detection in cotton crop for three types of diseases Alternaria Leaf Spot Fungal Disease (ALSFD), Grey Mildew Cotton Disease (GMCD), and Rust Foliar Fungal Disease (RFFD). The K-means clustering algorithm is used for disease segmentation for cotton leaf. The diseased cluster is segmented into three clusters. From cluster 2 the features Mean , Contrast, Energy, Correlation, Standard Deviation, Variance , Entropy, and Kurtosis are extracted. The extracted features for 30 samples are given to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers for disease classification. The performance of these classifiers are compared. The ALSF disease is classified 77.4% for ANN and 84.3% for SVM, GMC disease is 87.8% for ANN and 98.7% in SVM, RFF disease is 90.1%for ANN and 93.2% for SVM. The overall average accuracy of ANN classifier is 85.1% for three diseases and overall average accuracy for SVM is 92.06% for three diseases. It is clearly observed from the analysis SVM classifier gives accurate disease detection compared to ANN.
Keywords: Image Segmentation, K-Means Clustering, Alternaria Leaf Spot Fungal Disease, Grey-Mildew-Cotton Disease and Rust Foliar Fungal Disease, ANN, SVM
Scope of the Article: Classification