Two instance segmentation networks (Mask R-CNN) were trained to detect leaders-one using active lighting images and one using natural lighting images. Images were annotated for two classes of objects-trunks (horizontal base) and leaders. Stereo images of dormant sweet cherry ( Prunus avium L.) trees trained to the UFO architecture were collected using active and natural lighting. Our objectives were to: 1) develop an instance segmentation approach to detect leaders and estimate leader diameter in the UFO architecture, and 2) compare the performance of instance segmentation networks trained with active and natural lighting images.
One of the fundamental pruning rules in the Upright Fruiting Offshoots (UFO) architecture is to remove vigorous (i.e., large diameter) leaders. Deep neural networks are powerful tools in developing robust machine vision systems for orchard environments, and herein we demonstrate how deep convolutional neural networks can be used in an automated pruning system. Any automated pruning system must possess robust machine vision capable of making accurate pruning decisions in the complex orchard environment.
As a result, there is great interest in automated pruning for modern tree fruit orchards. However, pruning is laborious, requiring substantial human resources more than 40 h are required per acre of cherries. Pruning is a perennial orchard operation vital to orchard health, fruit yield, and fruit quality.
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