Crop Disease Detection Using YOLOv8

The outcomes of the crop disease detection are below. Using the YOLOv8 project highlights the important features.

• Successfully trained the YOLOv8 model to accurately detect crop diseases from images.

• Developed an efficient system for real-time crop disease detection. It is suitable for agricultural applications.

• Improved the model by making sure that a varied dataset was used to reflect different lighting situations and levels of cataract severity.

• Utilized Google Colab's GPU resources to speed up model training and optimize computational efficiency.

• Achieved high performance on unseen data using metrics such as precision, recall, and mean Average Precision (mAP).

• Overfitted by early stopping and dynamic learning rate scheduling.

• Provided a clear, accurate output by detecting diseases with bounding boxes on crop images.

• Developed a reliable model that can assist farmers in early and accurate crop disease diagnosis.

• Demonstrated the practical application of YOLOv8 in agriculture and farming automation.