Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation

TL;DR

This paper introduces an automated system for grapevine winter pruning that utilizes 2D plant modeling and segmentation to identify potential pruning points, aiming to reduce labor time in large vineyards.

Contribution

It presents a novel multidisciplinary approach combining neural network segmentation, plant modeling, and pruning point detection for grapevine pruning automation.

Findings

Successful segmentation of grapevine images using a fine-tuned neural network.

Generation of a representative grapevine plant model from segmented images.

Identification of a significant set of potential pruning points on grapevine canes.

Abstract

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80-120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of potential pruning points is generated from this plant representation. We will describe (a) a methodology for data acquisition and annotation, (b) a neural network fine-tuning for grapevine segmentation, (c) an image processing based method for creating the representative model of grapevines, starting from the inferred segmentation and (d) potential pruning points detection and localization, based on the plant model which is a simplification of the grapevine structure. With this approach, we are able to identify a significant set of potential pruning points on the canes, that can be used, with further selection, to derive the final set of the real pruning points.