Plant disease detection using deep learning is a rapidly growing research area in agricultural AI that focuses on identifying and classifying plant diseases from leaf images, fruit images, or crop-field data using advanced neural networks. Traditional methods relying on manual inspection are labor-intensive, time-consuming, and error-prone, whereas deep learning approaches automate feature extraction and classification, improving accuracy and scalability. Early research employed convolutional neural networks (CNNs) for image-based disease classification, while recent studies integrate advanced architectures such as residual networks (ResNet), DenseNet, attention mechanisms, and vision transformers (ViTs) to enhance detection performance under challenging conditions like varying lighting, occlusion, and complex backgrounds. Applications span precision agriculture, early disease warning systems, yield optimization, and automated crop monitoring using drones or smartphone-based platforms. Current research also explores data augmentation, transfer learning, multimodal fusion (combining images with environmental data), and lightweight models for deployment on edge devices, establishing deep learning as a key enabler for efficient, scalable, and accurate plant disease detection.