Title :
“Diagnosis and classification of grape leaf diseases using neural networks”
Author :
Sannakki, Sanjeev S. ; Rajpurohit, V.S. ; Nargund, V.B. ; Kulkarni, Parag
Author_Institution :
Dept. of Comput. Sci., Gogte Inst. of Technol. Belgaum, Belgaum, India
Abstract :
Plant diseases cause significant damage and economic losses in crops. Subsequently, reduction in plant diseases by early diagnosis results in substantial improvement in quality of the product. Erroneous diagnosis of disease and its severity leads to inappropriate use of pesticides. The goal of proposed work is to diagnose the disease using image processing and artificial intelligence techniques on images of grape plant leaf. In the proposed system, grape leaf image with complex background is taken as input. Thresholding is deployed to mask green pixels and image is processed to remove noise using anisotropic diffusion. Then grape leaf disease segmentation is done using K-means clustering. The diseased portion from segmented images is identified. Best results were observed when Feed forward Back Propagation Neural Network was trained for classification.
Keywords :
agriculture; agrochemicals; artificial intelligence; backpropagation; crops; economics; feedforward neural nets; image segmentation; plant diseases; artificial intelligence; crops; economic loss; feedforward backpropagation neural network; grape leaf diseases classification; grape leaf diseases diagnosis; image processing; image segmentation; image thresholding; neural networks; pesticides; plant diseases; Agriculture; Diseases; Feature extraction; Image color analysis; Image segmentation; Pipelines; Training; Feed forward neural network; co-occurrence matrix; feature extraction; image processing; k-means; plant disease identification;
Conference_Titel :
Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on
Conference_Location :
Tiruchengode
Print_ISBN :
978-1-4799-3925-1
DOI :
10.1109/ICCCNT.2013.6726616