Deep learning based approach for classification and detection of papaya leaf diseases

Abstract

In recent years, around the globe the horticulture crop outcomes falling down due to the devastating diseases, this impact will shows on yield of farmers such as quality and quantity of horticulture products, even in developed countries. Therefore, for prevention early observation and discovery of these diseases are very significant. In this paper we built a straight forward Convolution neural network on image classification for plant diseases, specifically for papaya plants, papaya suffering from Leaf Curl of Papaya, papaya mosaic. In a row, we propose for identification and classifying papaya leaves diseases a deep learning-based approach by using ResNet50 architecture as a convolutional neural network to stratify image data sets. Across globe in many disciplines deep learning has been employed. I.e. object tracking, text detection, image classification, action recognition. In deep learning different type of models, among Convolutional neural networks and Deep Belief Networks are frequently used models Convolutional neural networks has been exhibited extreme capabilities on image classification. The proposed model generated results are shown very usefulness of it, even under difficult conditions such as image size, pose, different resolution, illumination, complex back ground and alignment of actual images.