The agricultural output and global food security have greatly been concerned by the apple plant diseases. This research examines the probability of applying deep learning, particularly the InceptionV3 model with L2 regularizer (0.01 strength), for image-centric apple plant diseases (like apple scab, black rot, and cedar apple rust) detection and classification. The current study builds groundwork for the possible or inventive agricultural techniques to manage crop health effectively despite environmental and economic adversities. The approach proposed by us includes data collection, image preprocessing and the advancement of a deep learning model based on transfer learning and achieves 97% accuracy. The paper concludes by giving attention to optimal model architectures, neural network techniques, and dataset enlargement techniques as future applications in the agricultural field.