研究目的
To locate the center of plants from UAV images by classifying each pixel as either a plant center or not a plant center using a Convolutional Neural Network (CNN).
研究成果
The method works well on datasets with less overlap between plants but has more false alarms on datasets with high overlap. Future work will include incorporating the segmentation of individual leaves to find the center of plants.
研究不足
The method performs less effectively on datasets with high overlap between plants. Human ground truthing may miss some true plant centers due to occlusion, creating a bias in the labels.
1：Experimental Design and Method Selection:
The method involves classifying each pixel location in the image as a plant center or not by considering a rectangular window around the pixel. A CNN is used for classification.
2：Sample Selection and Data Sources:
Two datasets with sorghum plants at different growth stages were used, collected by a UAV platform.
3：List of Experimental Equipment and Materials:
UAV for image collection, CNN for classification.
4：Experimental Procedures and Operational Workflow:
Image patches are extracted and classified with a CNN. Post-processing involves applying connected components and morphological operations to locate plant centers.
5：Data Analysis Methods:
Precision and recall are used to evaluate the method.
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