Apple Leaf Disease Prediction Using Deep Learning Models

DOI

https://www.doi.org/10.59256/ijsreat.20240402007

Published In

Volume 4, Issue 2 (2024)

Published Online

March-April 2024

Pages

53-58

Article Type

Research Article

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: In India, nearly two lakh people rely on apple production for a living, with apples accounting for approximately 16% of GDP in their respective states. Although apple production is second only to that of other fruits in terms of volume, it generates more revenue. In addition, India ranks fifth in total apple production worldwide. As a result, the apple is a fruit that has a significant impact on our economic situation. Apple productivity is primarily influenced by leaf diseases such as scab, cedar rust, black rot, and others. To prevent apple leaf diseases from spreading throughout the tree, it is critical to detect them in their early stages of infection. We proposed a convolutional neural network methodology to accurately and efficiently detect these leaf diseases. The image pre-processing and augmentation methods help us distinguish the region of interest from the background. We then trained and validated our dataset of apple leaf images by applying deep learning algorithms. The Dense Net algorithm, which has a 98.42% accuracy rate, is used to label the leaf diseases in our model.