Computers and Electronics in Agriculture
A cognitive vision approach to early pest detection in greenhouse crops
Computers and Electronics in Agriculture
The Recognition of Cucumber Disease Based on Image Processing and Support Vector Machine
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Grading Method of Leaf Spot Disease Based on Image Processing
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 06
Digital Image Processing Using MATLAB: AND " Mathworks, MATLAB Sim SV 07 "
Digital Image Processing Using MATLAB: AND " Mathworks, MATLAB Sim SV 07 "
Identification of citrus disease using color texture features and discriminant analysis
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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In this paper, we present a novel algorithm for extracting lesion area and application of neural network to classify seedling diseases such as anthracnose and frog-eye spots on tobacco leaves. The lesion areas with anthracnose and frog-eye spots on a leaf of tobacco seedlings are segmented by contrast stretching transformation with an adjustable parameter and morphological operations. First order statistical texture features are extracted from lesion area to detect and diagnose the disease type. These texture features are then used for classification purpose. A Probabilistic Neural Network (PNN) is employed to classify anthracnose and frog-eye spots present on tobacco seedling leaves. In order to corroborate the efficacy of the proposed model we have conducted an experimentation on a dataset of 800 extracted areas of tobacco seedling leaves which are captured in an uncontrolled lighting conditions. The methodology presented herein effectively detected and classified the tobacco seedlings lesions upto an accuracy of 88.5933%. Further the recommended features are compared with Gray Level Co-occurrence Matrix (GLCM) based features to bring out their superiorities.