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Review

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

1
Institute for Future Farming Systems, Central Queensland University, Rockhampton 4701, Australia
2
Geco Enterprises Ltd., San Vicente de Tagua, Tagua 2970000, Chile
*
Author to whom correspondence should be addressed.
Academic Editor: Xiangjun Zou
Agronomy 2021, 11(7), 1409; https://doi.org/10.3390/agronomy11071409
Received: 27 May 2021 / Revised: 7 July 2021 / Accepted: 8 July 2021 / Published: 14 July 2021
(This article belongs to the Special Issue In-Field Estimation of Fruit Quality and Quantity)
The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted. View Full-Text
Keywords: yield; estimation; machine vision; remote sensing; correlative; models; fruit; tree; review yield; estimation; machine vision; remote sensing; correlative; models; fruit; tree; review
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MDPI and ACS Style

Anderson, N.T.; Walsh, K.B.; Wulfsohn, D. Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time. Agronomy 2021, 11, 1409. https://doi.org/10.3390/agronomy11071409

AMA Style

Anderson NT, Walsh KB, Wulfsohn D. Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time. Agronomy. 2021; 11(7):1409. https://doi.org/10.3390/agronomy11071409

Chicago/Turabian Style

Anderson, Nicholas T., Kerry B. Walsh, and Dvoralai Wulfsohn. 2021. "Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time" Agronomy 11, no. 7: 1409. https://doi.org/10.3390/agronomy11071409

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