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Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV

1
CIRAD, UPR HortSys, F-34398 Montpellier, France
2
HortSys, University Montpellier, CIRAD, F-34090 Montpellier, France
3
Centre pour le Développement de l’Horticulture, ISRA, Dakar 14000, Senegal
4
Université Cheikh Anta Diop (UCAD), Fac. Sci. Tech., Dakar BP 5005, Senegal
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1900; https://doi.org/10.3390/rs10121900
Received: 23 October 2018 / Revised: 23 November 2018 / Accepted: 26 November 2018 / Published: 28 November 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards. View Full-Text
Keywords: unmanned aerial vehicle; mango orchard; yield estimation; fruit detection; tree architecture; random forest; GEOBIA; structure-from-motion unmanned aerial vehicle; mango orchard; yield estimation; fruit detection; tree architecture; random forest; GEOBIA; structure-from-motion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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MDPI and ACS Style

Sarron, J.; Malézieux, É.; Sané, C.A.B.; Faye, É. Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV. Remote Sens. 2018, 10, 1900.

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