A methodology for detection and localization of fruits in apples orchards from aerial images.
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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.
Organizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021.
Organizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021.
Palabras clave
Redes neurais, Contagem automática de frutas, Detecção de maçãs, Convolutional neural networks, Fruit detection, Maçã, Neural networks, Apples