Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn