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Mango_deep_yield_dataset koirala et al. 2021

Central Queensland University
Anand Koirala (Aggregated by) Kerry Walsh (Aggregated by) Zhenglin Wang (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25946/14033993.v1&rft.title=Mango_deep_yield_dataset koirala et al. 2021&rft.identifier=10.25946/14033993.v1&rft.publisher=Central Queensland University&rft.description=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 treesB= 6 treesC= 12 treesABC= 35 treesA-x= 44 treesB-x= 19 treesC-x= 35 treesABC-x = 98 treesharvest_data_deep_yield.xlsx file contains the harvest fruit count mapped with the tree and image names for each orchard.&rft.creator=Anand Koirala&rft.creator=Kerry Walsh&rft.creator=Zhenglin Wang&rft.date=2021&rft_rights= https://creativecommons.org/licenses/by/4.0/&rft_subject=Computer vision&rft_subject=Applied computing not elsewhere classified&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=mango fruits&rft_subject=Mango fruit yield&rft_subject=image processing applications&rft_subject=deep learning dataset&rft_subject=Deep Learning Applications&rft_subject=computer vision technique&rft_subject=Computer Vision&rft_subject=Applied Computer Science&rft_subject=Artificial Intelligence and Image Processing&rft.type=dataset&rft.language=English Access the data
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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.

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Applied Computer Science | Applied computing not elsewhere classified | Artificial Intelligence and Image Processing | Artificial intelligence not elsewhere classified | Computer Vision | Computer vision | Deep Learning Applications | Mango fruit yield | computer vision technique | deep learning dataset | image processing applications | mango fruits |

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