Crop disease detection based on enhanced YOLOv8

Plant diseases have a great impact on global agricultural production and ecological security, which can cause the reduction of agricultural production and quality, and also bring food security problems. Traditional methods for detecting crop diseases rely heavily on manual observation and judgment, which suffer from issues such as long processing times, low efficiency, and dependence on expertise. In contrast, deep learning-based detection methods automate the identification and detection of crop disease images through training neural network models, offering advantages such as rapid processing speed, high accuracy, and enhanced efficiency. Therefore, this study improves the yolov8 network on the basis of deep learning to establish an accurate, stable and fast network detection model to provide a fast and efficient disease diagnosis solution for disease detection.