Classification of healthy and rot leaves of apple using gradient boosting and support vector classifier

Abstract

Conventional Techniques Such As Convolutional Neural Network (Cnn), Deep Neural Network Have Shown Its Own Footprints In The Field Of Image Classification With Promising Results. In The Past Decades, Classification Of Images Has Been Done With Varying Features Like Shape, Texture Etc. In This Paper, A Novel Approach Is Used To Classify The Leaf Images And Determine The Health And The Diseased Leaf. The Image Is Preprocessed By Extracting The Shape Feature And Classified The Leaves Of Apple As Healthy And Diseased (Rot Leaves) Using Two Novel Effective Approaches Gradient Boosting And Support Vector Classifier. We Have Collected 1813 Images Of Apple Leaves As Dataset And Out Of These, 70% Of The Data Is Used To Train And Remaining 30% Is Used To Test The Data. Our Algorithm Has Outperformed Other Traditional Techniques With Good Scale Of Accuracy(Gradient Boosting-87%, Support Vector Classifier-91%). Strong Comparison Of Both Gradient Boosting And Support Vector Is Made And There Is Dominant Show Off Of The Confusion Matrix. Classification Of Healthy And Diseased Leaf Well In Advance Gives Nice Warning To The Producer Thereby Decreasing The Rate Of Diseased.