Crop Disease Detection Using YOLOv8
This project utilizes YOLOv8 to build a crop disease detection and classification system in Google Colab. The system processes images and videos to identify diseases, providing an interactive interface for real-time analysis using Gradio.
$15 USD
$3.00 USD
Project Template Outcomes
The outcomes of the crop disease detection are below. Using the YOLOv8 project highlights the important features.
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Successfully trained the YOLOv8 model to accurately detect crop diseases from images.
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Developed an efficient system for real-time crop disease detection. It is suitable for agricultural applications.
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Improved the model by making sure that a varied dataset was used to reflect different lighting situations and levels of cataract severity.
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Utilized Google Colab's GPU resources to speed up model training and optimize computational efficiency.
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Achieved high performance on unseen data using metrics such as precision, recall, and mean Average Precision (mAP).
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Overfitted by early stopping and dynamic learning rate scheduling.
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Provided a clear, accurate output by detecting diseases with bounding boxes on crop images.
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Developed a reliable model that can assist farmers in early and accurate crop disease diagnosis.
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Demonstrated the practical application of YOLOv8 in agriculture and farming automation.
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