NAIVE BAYES CLASSIFIER TO IDENTIFY DISEASE IN FRUITS
DOI:
https://doi.org/10.65009/5as4gt06Keywords:
K-means clustering algorithm, intensity ratio, specificity ratio, probability ratio, fruit disease, SURF (speedup robust feature), NN(Neural Network) etc.,,Abstract
— Fruit infections are a serious issue that hurts the agriculture industry and the economy. In the
past, tainted fruit had to be manually identified; but, as technology has advanced, image processing
technologies have been created. This system operates in two stages: training and testing. The testing step
determines whether the fruit is contaminated and, if so, by which illness. The training phase stores all
data pertaining to both infected and non-infected fruit. This work developed a method for identifying
infected and non-infected fruit by combining the K-mean clustering algorithm, the speedup robust feature
(SURF) feature detector, and the Nave Bayes Classifier. A database of fruits is used for the investigations,
and the results are contrasted with those of a neural network. The outcomes show how effective the Nave
Bayes Classifier approach is. In recent years, fruit illnesses have been identified using clustering and fruit
picture segmentation approaches. To illustrate the significance of an algorithm graphic, multiple
estimations are used. Examples of ratios include the probability ratio, specificity ratio, and intensity ratio.
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