Precision farming, an innovative approach in agriculture, is poised to revolutionize the industry. A crucial aspect of precision farming is the early detection of leaf diseases, which can significantly impact crop yields and environmental sustainability. This survey paper explores the field’s state of the art, focusing on applying deep learning and transfer learning techniques for leaf ease detection. In our literature review, we delve into the various facets of this research domain. We discuss the effectiveness of deep learning models, particularly convolutional neural networks (CNNs), designed to identify plant diseases. Transfer learning, where pretrained models are fine-tuned for disease recognition, is also emphasized as a critical strategy to accelerate model convergence and efficiency. High-quality datasets, such as Plant Village and the National Agriculture Imagery Program (NAIP), play a pivotal role in training and evaluating these models. We showcase real-world applications of deep learning in precision farming, where disease detection is integrated into autonomous drones for real-time crop health monitoring. The potential benefits of such applications are discussed in the context of sustainable agriculture and reduced pesticide use. Despite the remarkable progress, we also acknowledge the persistent challenges and limitations in the field, such as limited access to diverse datasets, generalization across environmental conditions, and the computational resources required. Addressing these challenges is paramount to realizing the full potential of deep learning for leaf disease detection in precision farming.