Machine Learning Based Image Classification of Papaya Disease Recognition

Abstract

To help farmers and rural people of Bangladesh, many research works are proposed in the recent years to recognize the papaya diseases that takes a great deal of advantage in machine learning fields. This research is mainly required to support agriculture to make it highly effective and helpful particularly for papaya cultivation. The primary objective of this paper is to compare some algorithms for papaya disease recognition and identify the ailment by capturing image and classify them based on their diseases with an intelligent system. To overcome this advantage, the recognition of papaya diseases will mainly involve two challenges and those are detecting the disease and classifying the diseases based on their symptoms. The proposed system is presenting an online machine learning based papaya disease in which a person captures an image via mobile app and sends it to the system for disease detection and also compare some algorithms accuracy those are random forest, k-means clustering, SVC and CNN. The system process the images and will give feedback. This intelligent system can easily detect the diseases with a high accuracy of about 98.4% to predict the papaya diseases.