A methodology for detection and localization of fruits in apples orchards from aerial images

• URL: http://arxiv.org/abs/2110.12331v1
• Date: Sun, 24 Oct 2021 01:57:52 GMT
• Title: A methodology for detection and localization of fruits in apples orchards from aerial images
• Authors: Thiago T. Santos and Luciano Gebler
• Abstract summary: This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available.

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