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Attempting to estimate the unseen: Correction for occluded fruit in tree fruit load estimation by machine vision with deep learning

journal contribution
posted on 07.11.2021, 23:10 by Anand KoiralaAnand Koirala, Kerry WalshKerry Walsh, Zhenglin WangZhenglin Wang
Machine vision from ground vehicles is being used for estimation of fruit load on trees, but a correction is required for occlusion by foliage or other fruits. This requires a manually estimated factor (the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruits. Several image features, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest count of fruit per tree. A R2 of 0.98 (n = 98 trees) was achieved for the correlation of fruit count predicted by a Random forest model and the ground truth fruit count, compared to a R2 of 0.68 for the reference method. Error on prediction of whole orchard (880 trees) fruit load compared to packhouse count was 1.6% for the MLP model and 13.6% for the reference method. However, the performance of these models on data of another season was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

11

Issue

2

Start Page

1

End Page

20

Number of Pages

20

eISSN

2073-4395

Publisher

MDPI

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

10/02/2021

Author Research Institute

Institute for Future Farming Systems

Era Eligible

Yes

Journal

Agronomy

Article Number

347