Using YOLOv7 for crop and weed detection
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Updated
Mar 20, 2025 - Jupyter Notebook
Using YOLOv7 for crop and weed detection
A curated collection of 45 high-quality RGB image datasets for computer vision in agriculture. Features datasets for weed detection, disease identification, and crop monitoring, focusing on natural field scenes. Part of our GIL 2025 survey paper.
A Python-based machine learning application for classifying wheat species using image data.
Agrorader farm management software enables large businesses to have complete control over their farming processes across different stakeholders.
Computer Vision pipeline designed for precision agriculture applications, featuring automated dataset processing, advanced data augmentation, hyperparameter optimization, and edge-optimized model deployment for real-time crop and weed detection.
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