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

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.

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

“…Typically, the characterization of individual trees in orchards based on images from UAVs involves the estimation of tree numbers [26,27], tree height [28,29], and tree-crown parameters [21,29], which provide key indicators of plant growth [18] and plant yield [30] as well as for the assessment of pruning effects [24]. The most important parameters for the characterization of individual trees in orchards require the detection of individual trees and the delineation of individual tree-crowns.…”

Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

“…High-resolution digital surface models (DSMs) generated from sUAS data allow accounting for topo-edaphic conditions in forage production modeling and related analysis. A few pilot studies have explored the applications of sUAS technology in monitoring agricultural production for soybean [37], rice and wheat [38,39], barley [40,41], and mango [42]. However, there is currently limited research concerning the efficacy of sUASs for rangeland forage production quantification and modeling.…”

Birds and bats contribute to natural regulation of the millet head miner in tree-crop agroforestry systems

“…Drone photogrammetry and object-based image analysis procedures were used to characterize each site following the method described in Sarron et al (2018). Briefly, each site was overflowed using a UAV (DJI Mavic Pro, DJI Inc, China) in order to compute a high resolution orthomosaic (1.30 cm.pixel -1 ) and a Canopy Height Model (CHM) using a photogrammetry procedure.…”