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AI in Agriculture - Episode 2: Maize Seedling Quality Detection via UAV and Deep Learning
In today's episode we discuss study investigates a new method for assessing the quality of maize seedlings using high-resolution drone imagery.
It combines a deep learning object detection algorithm, YOLO v8, with Voronoi spatial analysis to extract three key indicators: the Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate.
The authors compare their method to traditional plant spacing analysis and demonstrate its superior accuracy, particularly in detecting missing seedlings.
The research highlights the potential of this method for improving agricultural production efficiency and reducing labour costs.
Hosted on Acast. See acast.com/privacy for more information.
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By Maryna KuzmenkoIn today's episode we discuss study investigates a new method for assessing the quality of maize seedlings using high-resolution drone imagery.
It combines a deep learning object detection algorithm, YOLO v8, with Voronoi spatial analysis to extract three key indicators: the Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate.
The authors compare their method to traditional plant spacing analysis and demonstrate its superior accuracy, particularly in detecting missing seedlings.
The research highlights the potential of this method for improving agricultural production efficiency and reducing labour costs.
Hosted on Acast. See acast.com/privacy for more information.