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Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery

  • N. T. Anderson
  • J. P. Underwood
  • M. M. Rahman
  • A. Robson
  • K. B. Walsh
Article
  • 77 Downloads

Abstract

In current best commercial practice, pre-harvest fruit load predictions of mango orchards are provided based on a manual count of fruit number on up to 5% of trees within each block. However, the variability in fruit number per tree (coefficient of variation, CV, from 27 to 93% across ten orchards) was demonstrated to be such that the best case commercial sampling practice was inadequate for reliable estimation (to an error of 54–82 fruit/tree, and percentage error, PE, of 10% at a probability of 0.95). These results highlight the need for alternative methods for estimation of orchard fruit load. Pre-harvest fruit load was estimated for a case study orchard of 469 trees using (i) count of a sample of trees, (ii) in-field machine vision and (iii) correlation to a tree spectral index estimated using high resolution satellite imagery. A count of 5% of trees (23) in the trial orchard resulted in a PE of 31% (error of 37 fruit/tree), with a count of 157 trees required to achieve a PE of 10% (error of 12 fruit/tree). Sampling effort to achieve a PE of 10% was decreased by only 10% by sampling from aspatial k-means tree classifications based on machine vision derived fruit counts of all trees. Clustering based on tree attributes of canopy volume and trunk circumference was not helpful in decreasing sampling effort as these attributes were poorly correlated to fruit load (R2 = 0.21 and 0.17, respectively). In-field multi-view machine vision-based estimation of fruit load per tree achieved a R2 = 0.97 and a RMSE = 14.8 fruit/tree against harvest fruit count per tree for a set of 18 trees (average = 88; SD = 82 fruit/tree), using a faster region convolutional neural network trained the previous season. The relationship between WorldView-3 (WV3) satellite spectral reflectance characteristics of sampled trees and fruit number was characterised by a R2 = 0.66 and a RMSE = 56.1 fruit/tree. For this orchard, for which the actual fruit harvest was 56,720 fruit, the estimate based on a manual count of 5% of trees was 47,955 fruit, while estimates based on 20 iterations of stratified sampling (of 5% of trees in each cycle) had variation (SD) of 9597. The machine vision method resulted in an estimate of 53,520 (SD = 1960) fruit and the remote sensing method, 51,944 (SD = 26,300) fruit for the orchard.

Keywords

Fruiticulture Getis–Ord Precision horticulture Remote sensing Tree crop Yield estimation Machine vision 

Notes

Acknowledgements

Support of Simpsons and Philpots Farms, Childers, Queensland, and Acacia Hills Farms and Tou’s Garden, Northern Territory, is appreciated. Sushil Pandey is acknowledged for field measurements on the Childers farm. Mila Bristow and the NT Department of Primary Industry (Plant Industries) is acknowledged for field harvest measurements on Northern Territory farms. This work was supported by the Australian Centre for Field Robotics (ACFR) at the University of Sydney, the Precision Agriculture Group at University of New England and the Institute for Future Farming Systems at Central Queensland University. Funding support (Grants ST15002, ST15005 and ST15006) from Horticulture Industry Australia and the Australian Government Department of Agriculture and Water Resources as part of its Rural R&D Profit program is acknowledged.

References

  1. Esri. (2018). An overview of the spatial statistics toolbox. Retrieved September 9, 2018, from http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/an-overview-of-the-spatial-statistics-toolbox.htm. Redlands, CA, USA.
  2. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.CrossRefGoogle Scholar
  3. Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306.CrossRefGoogle Scholar
  4. Payne, A. B., Walsh, K. B., Subedi, P. P., & Jarvis, D. (2013). Estimation of mango crop yield using image analysis—Segmentation method. Computers and Electronics in Agriculture, 91, 57–64.CrossRefGoogle Scholar
  5. Peeters, A., Zude, M., Käthner, J., Ünlü, M., Kanber, R., Hetzroni, A., et al. (2015). Getis–Ord’s hot-and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Computers and Electronics in Agriculture, 111, 140–150.CrossRefGoogle Scholar
  6. Rasmussen, C., & Williams, C. (2006). Gaussian processes for machine learning (pp. 7–13). Massachusetts, USA: The MIT Press.Google Scholar
  7. Robson, A., Rahman, M. M., & Muir, J. (2017). Using worldview satellite imagery to map yield in avocado (Persea americana): A case study in Bundaberg, Australia. Remote Sensing, 9(12), 1223.CrossRefGoogle Scholar
  8. Robson, A. J., Petty, J., Joyce, D. C., Marques, J. R., & Hofman, P. J. (2016). High resolution remote sensing, GIS and Google Earth for avocado fruit quality mapping and tree number auditing. In Proceedings of the 29th international horticultural congress (IHC2014) (pp. 589–595). Leuven, Belgium: ISHS.Google Scholar
  9. Stein, M., Bargoti, S., & Underwood, J. (2016). Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors, 16(11), 1915.CrossRefGoogle Scholar
  10. Taylor, J. A., Praat, J. P., & Bollen, A. F. (2007). Spatial variability of kiwifruit quality in orchards and its implications for sampling and mapping. HortScience, 42(2), 246–250.Google Scholar
  11. Thrusfield, M. (1995). Veterinary epidemiology (2nd ed.). Oxford, UK: Blackwell Science Ltd.Google Scholar
  12. Yadav, I., Rao, N. S., Reddy, B., Rawal, R., Srinivasan, V., Sujatha, N., et al. (2002). Acreage and production estimation of mango orchards using indian remote sensing (IRS) satellite data. Scientia Horticulturae, 93(2), 105–123.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • N. T. Anderson
    • 1
  • J. P. Underwood
    • 2
  • M. M. Rahman
    • 3
  • A. Robson
    • 3
  • K. B. Walsh
    • 1
  1. 1.Institute for Future Farming SystemsCentral Queensland UniversityRockhamptonAustralia
  2. 2.Australian Centre for Field RoboticsUniversity of SydneySydneyAustralia
  3. 3.Precision Agriculture GroupUniversity of New EnglandArmidaleAustralia

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