Abstract
High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bottlenecks is the presence of occluded flowers due to limitations of the top-view perspective of the platform-sensor combinations. In order to tackle this problem, the multi-view perspective from the Red–Green–Blue (RGB) colored dense point clouds retrieved from drone imagery are compared and evaluated against the field-based flower cluster number per tree. Experimental results obtained on a dataset of two pear tree orchards (N = 144) demonstrate that our 3D object-based method, a combination of pixel-based classification with the stochastic gradient boosting algorithm and density-based clustering (DBSCAN), significantly outperforms the state-of-the-art in flower cluster estimations from the 2D top-view (R2 = 0.53), with R2 > 0.7 and RRMSE < 15%.
Highlights
In fruit orchards, spatial explicit flowering information is key in guiding the processes of pruning of branches and flower thinning, which directly impact crop load, fruit size, coloration, and taste.Visual flower counting is currently the most common approach for bloom intensity estimation in orchards, but is a technique which is time consuming, labor-intensive and prone to errors if not done by experts
The parameters k and ε are optimized for clustering the flower pixels into flower object with the use of DBSCAN (Section 3.3)
This study has shown that drone imagery has a huge potential for flower cluster estimations
Summary
Spatial explicit flowering information is key in guiding the processes of pruning of branches and flower thinning, which directly impact crop load, fruit size, coloration, and taste.Visual flower counting is currently the most common approach for bloom intensity estimation in orchards, but is a technique which is time consuming, labor-intensive and prone to errors if not done by experts. Since only a limited sample of the trees is inspected, extrapolation to the entire orchard relies strongly on the grower’s experience as no information on the spatial variability within the orchards is provided [1,2,3]. These limitations, in combination with the short-term nature of flower appearance, make an automated method highly desirable. Imaging methods ranging from spaceborne to proximal platforms can deliver the inputs needed to automate flower mapping.
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