ABSTRACT
Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimize losses to productivity caused by it. Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This project proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient images and designing a novel architecture of a deep convolutional neural network based on image processing to detect apple leaf diseases. This project is developed in python using convolutional neural network.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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