Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm

In the agriculture field, one of the recent research topics is recognition and classification of diseases from the leaf images of a plant. The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural...

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Veröffentlicht in:Information processing in agriculture Jg. 7; H. 2; S. 249 - 260
Hauptverfasser: Ramesh, S., Vydeki, D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2020
Elsevier
Schlagworte:
algorithms
blight
Color features
crops
farmers
farms
foliar diseases
image analysis
Jaya optimization algorithm
K-means clustering
leaves
Optimized Deep Neural Network
Oryza sativa
paddies
Paddy leaf diseases
rice
Texture features
Optimized Deep Neural Network
Jaya optimization algorithm
Color features
Paddy leaf diseases
Texture features
K-means clustering
ISSN:2214-3173, 2214-3173
Online-Zugang:Volltext
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Zusammenfassung:In the agriculture field, one of the recent research topics is recognition and classification of diseases from the leaf images of a plant. The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products. In this paper, Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed. For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal, bacterial blight, brown spot, sheath rot and blast diseases. In pre-processing, for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased part. For the segmentation of diseased portion, normal portion and background a clustering method is used. Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm (DNN_JOA). In order to precise the stability of this approach a feedback loop is generated in the post processing step. The experimental results are evaluated and compared with ANN, DAE and DNN. The proposed method achieved high accuracy of 98.9% for the blast affected, 95.78% for the bacterial blight, 92% for the sheath rot, 94% for the brown spot and 90.57% for the normal leaf image.
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ISSN:2214-3173
2214-3173
DOI:10.1016/j.inpa.2019.09.002