Author(s): SANTOS, T. T.; GEBLER, L.

Summary: 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.

Publication year: 2021

Types of publication: Paper in annals and proceedings

Unit: Embrapa Digital Agriculture

Keywords: Apples, Contagem automática de frutas, Convolutional neural networks, Detecção de maçãs, Fruit detection, Maçã, Neural networks, Redes neurais