Rahman, Moshiur; Robson, Andrew; Salgadoe, Surantha; Walsh, Kerry; Bristow, Mila

Author(s)

Rahman, Moshiur

Robson, Andrew

Salgadoe, Surantha

Walsh, Kerry

Bristow, Mila

Publication Date

2020-04-07

Abstract

Accurate pre-harvest yield estimation of high value fruit tree crops provides a range of benefits to industry and growers. Currently, yield estimation in Avocado (<i>Persea americana</i>) and Mango (<i>Mangifera indica</i>) orchards is undertaken by a visual count of a limited number of trees. However, this method is labour intensive and can be highly inaccurate if the sampled trees are not representative of the spatial variability occurring across the orchard. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted ﬁeld sampling for the pre-harvest prediction of yield. A stratified sampling technique was applied in each block to measure relevant yield parameters from eighteen sample trees representing high, medium and low vigour zones (6 from each) based on classified normalised difference vegetation index (NDVI) maps. For avocado crops, principal component analysis (PCA) and non-linear regression analysis were applied to 18 derived vegetation indices (VIs) to determine the index with the strongest relationship to the measured yield parameters. For mango, an integrated approach of geometric (tree crown area) and optical (spectral vegetation indices) data using artiﬁcial neural network (ANN) model produced more accurate predictions. The results demonstrate that accurate maps of yield variability and total orchard yield can be achieved from WV imagery and targeted sampling; whilst accurate maps of fruit size and the incidence of phytophthora can also be achieved in avocado. These outcomes offer improved forecasting than currently adopted practices and therefore offer great benefit to both the avocado and mango industries.

Citation

Proceedings, 36(1), p. 1-1

ISSN

2504-3900

Link

https://hdl.handle.net/1959.11/30942

Publisher

MDPI AG

Rights

Attribution 4.0 International

Title

Exploring the Potential of High Resolution Satellite Imagery for Yield Prediction of Avocado and Mango Crops

Type of document

Conference Publication

Entity Type

Publication

Author(s)	Rahman, Moshiur Robson, Andrew Salgadoe, Surantha Walsh, Kerry Bristow, Mila
Publication Date	2020-04-07
Abstract	Accurate pre-harvest yield estimation of high value fruit tree crops provides a range of benefits to industry and growers. Currently, yield estimation in Avocado (<i>Persea americana</i>) and Mango (<i>Mangifera indica</i>) orchards is undertaken by a visual count of a limited number of trees. However, this method is labour intensive and can be highly inaccurate if the sampled trees are not representative of the spatial variability occurring across the orchard. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted ﬁeld sampling for the pre-harvest prediction of yield. A stratified sampling technique was applied in each block to measure relevant yield parameters from eighteen sample trees representing high, medium and low vigour zones (6 from each) based on classified normalised difference vegetation index (NDVI) maps. For avocado crops, principal component analysis (PCA) and non-linear regression analysis were applied to 18 derived vegetation indices (VIs) to determine the index with the strongest relationship to the measured yield parameters. For mango, an integrated approach of geometric (tree crown area) and optical (spectral vegetation indices) data using artiﬁcial neural network (ANN) model produced more accurate predictions. The results demonstrate that accurate maps of yield variability and total orchard yield can be achieved from WV imagery and targeted sampling; whilst accurate maps of fruit size and the incidence of phytophthora can also be achieved in avocado. These outcomes offer improved forecasting than currently adopted practices and therefore offer great benefit to both the avocado and mango industries.
Citation	Proceedings, 36(1), p. 1-1
ISSN	2504-3900
Link	https://hdl.handle.net/1959.11/30942
Publisher	MDPI AG
Rights	Attribution 4.0 International
Title	Exploring the Potential of High Resolution Satellite Imagery for Yield Prediction of Avocado and Mango Crops
Type of document	Conference Publication
Entity Type	Publication

Exploring the Potential of High Resolution Satellite Imagery for Yield Prediction of Avocado and Mango Crops

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