Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22501
Title: Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia
Contributor(s): Robson, Andrew  (author)orcid ; Rahman, Muhammad Moshiur  (author)orcid ; Muir, Jasmine  (author)orcid 
Publication Date: 2017
Open Access: Yes
DOI: 10.3390/rs9121223Open Access Link
Handle Link: https://hdl.handle.net/1959.11/22501
Abstract: Accurate pre-harvest estimation of avocado (Persea americana cv. Haas) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. 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 total fruit weight (kg·tree⁻¹) and average fruit size (g) and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI) derived from the WV images and sampled for total fruit weight (kg·tree⁻¹) and average fruit size (g) per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs) derived. Principal component analysis (PCA) and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017) a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1) to both total fruit weight (kg·tree⁻¹) (R² = 0.45, 0.28, and 0.29 respectively) and average fruit size (g) (R² = 0.56, 0.37, and 0.29 respectively) across all orchard blocks. Separate analysis of each orchard block produced higher R² values as well as identifying different optimal VIs for each orchard block and year. This suggests orchard location and growing season are influencing the relationship of spectral reflectance to total fruit weight and average fruit size. Classified maps of avocado yield (kg·tree⁻¹) and average fruit size per tree (g) were produced using the relationships developed for each orchard block. Using the relationships derived between the measured yield parameters and the optimal VIs, total fruit yield (kg) was calculated for each of the five sampled blocks for the 2016 and 2017 seasons and compared to actual yield at time of harvest and pre-season grower estimates. Prediction accuracies achieved for each block far exceeded those provided by the grower estimates.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 9(12), p. 1-20
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Fields of Research (FoR) 2008: 070104 Agricultural Spatial Analysis and Modelling
070106 Farm Management, Rural Management and Agribusiness
070699 Horticultural Production not elsewhere classified
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
300208 Farm management, rural management and agribusiness
Socio-Economic Objective (SEO) 2008: 829999 Plant Production and Plant Primary Products not elsewhere classified
820299 Horticultural Crops not elsewhere classified
970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
Socio-Economic Objective (SEO) 2020: 280101 Expanding knowledge in the agricultural, food and veterinary sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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