Real-Time Apple Disease Detection and Classification Using Hybrid CNN Model
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Keywords:
CNN, Machine Learning, Apple Disease Detection, Classification, RGB ImagesAbstract
Identifying and categorizing diseases in apple fruit is a difficult and time-consuming task in
the field of agriculture. It is crucial to have an automated method for detecting apple diseases to
effectively monitor and ensure sufficient and healthy production. While disease symptoms are visible in
the apple fruit, having experts diagnose them in a lab is expensive and time consuming. This paper
proposes a deep learning approach to detect and classify three types of common fungal diseases in apples
(apple scab, apple rot, and apple blotch) from Red Green Blue (RGB) images of apples taken at various
resolutions. The convolutional neural network model is used to distinguish between healthy and diseased
apples. Agriculture heavily relies on digital image processing and analysis to ensure the production of
high-quality fruits. Using CNN as a classifier to automatically detect and classify apple diseases, we have
experimentally proven the importance of pre-programmed knowledge in the agriculture industry. Cross
validation and testing on unseen data were conducted to exhaustively evaluate the trained model in
various parameters. The experimental results have demonstrated that the proposed deep learning-based
algorithm can accurately classify the three types of apple diseases with good accuracy.
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