• PRABIRA KUMAR SETHY
• KRISHNA PATEL
• Dr. Nalini kanta Barpanda
• Prof. Amiya Kumar Rath

On this paper, Bayesian Regularized Artificial Neural Network (BRANN) model was developed to detect rice disorders using the properties of the affected leaves. The work involves identifying healthy leaf and four types of disease, such as brown spot (BS), bacterial blight (BB), Leaf scald & Leaf blast (LB). The BRANN model was experimented with 100 number of samples with 20 number of each category. Here 75% data used as training purpose, 15% for validation and 15% for testing. Here k-means clustering used for extracting the diseased region, nine number of features extracted from affected region and then these features used for training and testing the model and achieve coefficient of regression, R-value 0.85.

rice leaf diseases identification; Bayesian regularized artificial neural network; image processing; texture feature.

"BRANN Model for Identification of Rice Leaf Diseases Using Texture Feature", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.569-573, May-2019, Available :http://www.jetir.org/papers/JETIR1905C83.pdf

"BRANN Model for Identification of Rice Leaf Diseases Using Texture Feature", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp569-573, May-2019, Available at : http://www.jetir.org/papers/JETIR1905C83.pdf