Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

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Aim:

To detect the apple leaf diseases using convolutional neural network for high accuracy detection

Existing System:

In recent years, with the popularization of digital cameras and other electronic devices, automatic plant disease diagnosis has been widely applied as a satisfactory alternative. Nevertheless, in most cases, traditional machine learning approaches such as support vector machine (SVM) and K-means clustering have complex image preprocessing and feature extraction steps, which reduce the efficiency of disease diagnosis.

Proposed System:

In this proposed system a deep learning approach that is based on improved convolution neural networks (CNN) for the real-time detection of apple leaf diseases. The proposed deep-learning based approach can automatically identify the discriminative features of the diseased apple images and detect the types of apple leaf diseases with high accuracy. At the same time the proposed approach can detect not only various diseases in the same diseased image but also the same disease of different sizes in the same diseased image.