This study presents a novel approach for detecting and identifying plant leaf diseases using the YOLOv4 algorithm. The research aims to improve the accuracy and efficiency of plant disease detection, which is crucial for reducing economic losses and maximizing crop yield. The study utilizes the Plant Village Dataset, which contains over 50,000 images of healthy and diseased plant leaves from 14 species. Data augmentation techniques, including histogram equalization and horizontal flip, were used to enhance the dataset and improve the model's resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, comparing its performance with established methods such as Densenet, Alexanet, and neural networks. The results showed that YOLOv4 achieved an impressive accuracy of 99.99% on the Plant Village dataset. The evaluation criteria, including accuracy, precision, recall, and F1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. The study's results demonstrate significant advancements in plant disease detection and highlight the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant implications for agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection. The study also discusses the importance of accurate plant disease detection in crop harvesting, as it helps minimize development problems and reduces the need for pesticides, supporting sustainable agricultural practices. The research highlights the potential of deep learning in this area of computer vision, particularly in detecting and identifying plant leaf diseases using YOLOv4. The study provides valuable insights into the practical implementation of the YOLOv4 architecture for detecting plant leaf disease, offering a robust and efficient solution for agricultural applications. The results show that the proposed methodology is robust against various input sample distortions, including noise, blurring, rotation, contrast changes, lighting adjustments, color inconsistencies, and brightness swings. The study also discusses the importance of data collection, model training, and multiple-class categorization of plant leaf disease in creating a framework for plant disease detection and classification. The research contributes to the field of precision agriculture by providing a reliable and efficient method for detecting and identifying plant leaf diseases. The study's findings demonstrate the effectiveness of YOLOv4 in accurately identifying and classifying plant leaf diseases, making it a valuable tool for agricultural applications. The results highlight the potential of YOLOv4 in improving plant disease detection and its ability to adapt to various scenarios, including those not encountered during training. The study also emphasizes the importance of accurate annotations and data preprocessing in achieving high performance in plant disease detection. The research provides a comprehensive overview of the methodology, including data collection, model design, training, and evaluation. The results show that the proposed approach is effective in detecting and identifying plant leaf diseases, with high accuracy and precision. The study concludes that YOLOv4 is a powerful tool for plant disease detection, offeringThis study presents a novel approach for detecting and identifying plant leaf diseases using the YOLOv4 algorithm. The research aims to improve the accuracy and efficiency of plant disease detection, which is crucial for reducing economic losses and maximizing crop yield. The study utilizes the Plant Village Dataset, which contains over 50,000 images of healthy and diseased plant leaves from 14 species. Data augmentation techniques, including histogram equalization and horizontal flip, were used to enhance the dataset and improve the model's resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, comparing its performance with established methods such as Densenet, Alexanet, and neural networks. The results showed that YOLOv4 achieved an impressive accuracy of 99.99% on the Plant Village dataset. The evaluation criteria, including accuracy, precision, recall, and F1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. The study's results demonstrate significant advancements in plant disease detection and highlight the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant implications for agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection. The study also discusses the importance of accurate plant disease detection in crop harvesting, as it helps minimize development problems and reduces the need for pesticides, supporting sustainable agricultural practices. The research highlights the potential of deep learning in this area of computer vision, particularly in detecting and identifying plant leaf diseases using YOLOv4. The study provides valuable insights into the practical implementation of the YOLOv4 architecture for detecting plant leaf disease, offering a robust and efficient solution for agricultural applications. The results show that the proposed methodology is robust against various input sample distortions, including noise, blurring, rotation, contrast changes, lighting adjustments, color inconsistencies, and brightness swings. The study also discusses the importance of data collection, model training, and multiple-class categorization of plant leaf disease in creating a framework for plant disease detection and classification. The research contributes to the field of precision agriculture by providing a reliable and efficient method for detecting and identifying plant leaf diseases. The study's findings demonstrate the effectiveness of YOLOv4 in accurately identifying and classifying plant leaf diseases, making it a valuable tool for agricultural applications. The results highlight the potential of YOLOv4 in improving plant disease detection and its ability to adapt to various scenarios, including those not encountered during training. The study also emphasizes the importance of accurate annotations and data preprocessing in achieving high performance in plant disease detection. The research provides a comprehensive overview of the methodology, including data collection, model design, training, and evaluation. The results show that the proposed approach is effective in detecting and identifying plant leaf diseases, with high accuracy and precision. The study concludes that YOLOv4 is a powerful tool for plant disease detection, offering