A methodology for detection and localization of fruits in apples orchards from aerial images
This work addresses yield estimation for apple farmers, but it is incremental as it builds on existing CNN-based detection methods by adding tracking and 3D localization.
The paper tackles the problem of accurate fruit counting and yield prediction in apple orchards using aerial images, achieving correlations above 0.8 between automated fruit counts and true yield.
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.