Machine Learning
An iterative algorithm for minimum cross entropy thresholding
Pattern Recognition Letters
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
A two-step circle detection algorithm from the intersecting chords
Pattern Recognition Letters
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A New Thresholding Algorithm Based on All-Pole Model
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
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Development of an automation system for classifying diseases of the infected plants is a growing research area in precision agriculture. The paper aims at classifying different types of rice diseases by extracting features from the infected regions of the rice plant images. Fermi energy based segmentation method has been proposed in the paper to isolate the infected region of the image from its background. Based on the field experts' opinions, symptoms of the diseases are characterized using features like colour, shape and position of the infected portion and extracted by developing novel algorithms. To reduce complexity of the classifier, important features are selected using rough set theory (RST) to minimize the loss of information. Finally using selected features, a rule base classifier has been built that cover all the diseased rice plant images and provides superior result compare to traditional classifiers.