Image Based Apple Diseases Detection Using Advanced Machine Learning Models

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. Seerat Un Nisa | Gurinder Kaur "Image-Based Apple Diseases Detection Using Advanced Machine Learning Models" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-6 , December 2025, URL: https://www.ijtsrd.com/papers/ijtsrd97700.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/97700/imagebased-apple-diseases-detection-using-advanced-machine-learning-models/seerat-un-nisa