Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array

Abstract

Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.

Overcoming the challenge of bunch occlusion by leaves for vineyard yield estimation using image analysis

“…The approach described in this work seems to be a viable method to estimate bunches hidden by leaves; however, it achieves acceptable accuracy in cases where a considerable portion of bunches are visible. High-density canopies with very low-to-none visible bunches remain a challenge that may only fully be surpassed with advanced sensing such as ultrasounds (Parr et al, 2021) or other devices that can see through leaves, but which are not yet readily available or practical to use in the field conditions.…”

“…The algorithm showed promising yet inconclusive results when applied to non-synthetic data (real vine images). Another technology was tested by Parr et al (2021), where ultrasonic phased arrays were used to overcome the problem of occluded bunches, showing results that appear promising in controlled conditions. However, despite all these research efforts, the challenge of bunch occlusion in vineyard conditions remains unsolved.…”

A Multicultivar Approach for Grape Bunch Weight Estimation Using Image Analysis

“…As mentioned before, all of the analyzed features could be collected from a vine image of a realistic, vineyard scenario [12,15,34,37]. However, we predict that the application of the studied approach in such conditions would be subject to several challenges/adaptations, such as (i) image resolution, which would be particularly important to extract features such as bunch perimeter and visible berries, as these require more detail and, thus, higher resolution if images are to be taken from a larger distance; (ii) bunch occlusion by leaves, where recent works have explored ways to estimate the occluded bunches [39,40], but this challenge still remains unsolved; (iii) extracting features from occluded bunches, as even if occluded bunches are estimated, it will be impossible to have their corresponding exact area, visible berries or perimeter, and ratios between these features on the visible portion of the bunches can be a better option; and (iv) robust segmentation methods, as this challenge lies in the step before the weight estimation (segmentation step), being crucial for a vineyard scenario.…”

Comparing a New Non-Invasive Vineyard Yield Estimation Approach Based on Image Analysis with Manual Sample-Based Methods

“…Considering the disadvantages of the manual methods regarding their cost, possible destructiveness and unreliable estimation error, recently, several efforts have been made to replace them with automatic non-invasive approaches. Several alternatives to manual vineyard yield estimation have been recently reported [17], for example, based on the use of vegetation indexes obtained via aerial imaging [18], airborne pollen samples obtained using Crus pollen traps [19,20], trellis tension sensors deployed across the vineyard [21], crop simulation models using environmental and physiological data [22] and sensor technology such as radar or ultrasonic for bunch detection in dense canopies [23,24]. Overall, all methods present advantages and disadvantages when compared to manual approaches.…”