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Computer vision methods based on convolutional neural networks (CNNs) havepresented promising results on image-based fruit detection at ground-level fordifferent crops. However, the integration of the detections found in differentimages, allowing accurate fruit counting and yield prediction, have receivedless attention. This work presents a methodology for automated fruit countingemploying aerial-images. It includes algorithms based on multiple view geometryto perform fruits tracking, not just avoiding double counting but also locatingthe fruits in the 3-D space. Preliminary assessments show correlations above0.8 between fruit counting and true yield for apples. The annotated datasetemployed on CNN training is publicly available.

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