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


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.
Palavras-chave: object detection, fruit detection, object localization, fruit localization, apples, yield prediction, fruit tracking, orchard monitoring


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SANTOS, Thiago T.; GEBLER, Luciano. A methodology for detection and localization of fruits in apples orchards from aerial images. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 13. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 1-9. ISSN 2177-9724. DOI: https://doi.org/10.5753/sbiagro.2021.18369.