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Mango_deep_yield_dataset koirala et al. 2021

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posted on 2021-02-15, 05:46 authored by Anand KoiralaAnand Koirala, Zhenglin WangZhenglin Wang, Kerry WalshKerry Walsh
Koirala, A.; Walsh, K.B.; Wang, Z. Attempting to Estimate the Unseen – Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning.

This dataset contains dual-view images (image from two opposite sides (sideA and sideB) of a tree) used in the paper "Attempting to estimate the unseen - correction for occluded fruit in tree fruit load estimation by machine vision with deep learning".
There are three orchards A, B and C with images of same trees from two seasons (2017 and 2018).
For each season ABC is the collection of images from orchards A, B and C put together.
A-x, B-x and C-x comprise of extended set of images collected in season 2017.

A= 17 trees
B= 6 trees
C= 12 trees
ABC= 35 trees

A-x= 44 trees
B-x= 19 trees
C-x= 35 trees
ABC-x = 98 trees

harvest_data_deep_yield.xlsx file contains the harvest fruit count mapped with the tree and image names for each orchard.

Funding

Project ST19009, Multiscale monitoring tools for managing Australian tree crops.

History

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2021-02-10

Author Research Institute

  • Institute for Future Farming Systems

Era Eligible

  • Yes