Apple farming plays a significant role in the agricultural economy but suffers considerable losses due to diseases affecting leaves and fruits. Manual identification of these diseases is often slow, inaccurate, and non-scalable. This review outlines recent advancements in image-based apple disease detection using machine learning (ML) and deep learning (DL). Emphasis is placed on feature extraction techniques, classification algorithms, and dataset curation. Convolutional neural networks (CNNs), support vector machines (SVMs), and transfer learning methods are examined for their effectiveness in detecting conditions such as apple scab, cedar apple rust, and compound infections. The paper also highlights key challenges and opportunities for future research in real-time applications and intelligent farming.
Apple disease, Image Processing, Machine Learning, CNN, SVM, Deep Learning, Smart Agriculture.
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