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.
Highlights
Mango (Mangifera indica L.) is a major fruit crop of the tropics and sub-tropics that guarantees the incomes and food security for local populations [1]
We develop a comprehensive set of tools for quantifying the structure of mango trees, and compute orchard land covers in order to accurately estimate and map the production at the orchard scale
Most of the processing time was spent on geographic object-based image analysis (GEOBIA) classification, which took from 4 h 30 to 8 h 10, depending on orchard complexity and whether or not the level 2 classification was applied
Summary
Mango (Mangifera indica L.) is a major fruit crop of the tropics and sub-tropics that guarantees the incomes and food security for local populations [1]. In West Africa, to meet an ever-increasing fruit demand from local and international markets [2], various mango cropping systems co-exist, from small, family-based, diversified orchards to large, commercial-based, monospecific orchards [3,4]. In this region, more than 20 polyembryonic and monoembryonic cultivars were featured by Rey et al [4]. Orchard production is measured by counting the number of harvested fruit buckets and multiplying it by an average weight These inaccurate assessments of yield and production are mostly conducted in commercial and homogeneous orchard plots, and rarely in small, diversified orchards. In the meantime, providing growers with accurate yield maps will inform them about precise farming management, and will help researchers to study the parameters driving yield heterogeneity within and between orchards [6,7]
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