Apple Fruit Disease Classification using Naive Bayes Algorithm

Abstract

Apples are one of the most productive varieties of fruit in the world, with a high nutritional and medicinal value. However, numerous diseases affect apple production on a wide scale, resulting in significant economic losses. These diseases often go overlooked until just before, after, or after fruit has been processed. Many pathogens can be avoided with cultural traditions and (optional) fungicides, even if there are no cures for tainted fruit. However, accurate diagnosis is essential for determining the right management practices and preventing further losses. Apple scab, apple rot, and apple blotch are some of the most prevalent diseases that affect apples. The proposed approach will greatly aid in the automated identification and classification of apple diseases, according to our test results. We discovered that normal apples were easy to discern from diseased apples in our trial, and that the texture-based GLCM function produced more reliable results for apple disease classification, with a classification accuracy of more than 96.43 percent. This demonstrates that combining the GLCM extraction function with naive bayes classification will greatly improve accuracy. Key Words: Naive bayes, Apple Diseases, Classifier