This paper investigates the use of the YOLOv4 algorithm for detecting and identifying plant leaf diseases, leveraging the comprehensive Plant Village Dataset, which includes over 50,000 images of healthy and diseased plant leaves from 14 different species. The study employs data augmentation techniques such as histogram equalization and horizontal flipping to enhance the dataset and improve the model's robustness. The YOLOv4 algorithm is compared with established methods like Densenet, Alexanet, and neural networks, achieving an impressive accuracy of 99.99% on the Plant Village dataset. The evaluation criteria, including accuracy, precision, recall, and F1-score, consistently show high performance with values of 0.99, confirming the effectiveness of the proposed methodology. The results demonstrate significant advancements in plant disease detection and highlight the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction, providing valuable insights for researchers and farmers in disease control and crop protection. The study also discusses the importance of precise image annotation, data preparation, and model training in achieving reliable disease identification.This paper investigates the use of the YOLOv4 algorithm for detecting and identifying plant leaf diseases, leveraging the comprehensive Plant Village Dataset, which includes over 50,000 images of healthy and diseased plant leaves from 14 different species. The study employs data augmentation techniques such as histogram equalization and horizontal flipping to enhance the dataset and improve the model's robustness. The YOLOv4 algorithm is compared with established methods like Densenet, Alexanet, and neural networks, achieving an impressive accuracy of 99.99% on the Plant Village dataset. The evaluation criteria, including accuracy, precision, recall, and F1-score, consistently show high performance with values of 0.99, confirming the effectiveness of the proposed methodology. The results demonstrate significant advancements in plant disease detection and highlight the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction, providing valuable insights for researchers and farmers in disease control and crop protection. The study also discusses the importance of precise image annotation, data preparation, and model training in achieving reliable disease identification.