A Multicultivar Approach for Grape Bunch Weight Estimation Using Image Analysis

Abstract

The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach. A set of 192 bunches from four cultivars, collected at sites located in Portugal and South Africa, were imaged using a conventional digital RGB camera, followed by image analysis, where several bunch features were extracted, along with physical measurements performed in laboratory conditions. Image data features were explored as predictors of bunch weight, individually and in a multiple stepwise regression analysis, which were then tested on 37% of the data. The results show that the variables bunch area and visible berries are good predictors of bunch weight (R2 ranging from 0.72 to 0.90); however, the simple regression lines fitted between these predictors and the response variable presented significantly different slopes among cultivars, indicating cultivar dependency. The elected multiple regression model used a combination of four variables: bunch area, bunch perimeter, visible berry number, and average berry area. The regression analysis between the actual and estimated bunch weight yielded a R2 = 0.91 on the test set. Our results are an important step towards automatic yield estimation in the vineyard, as they increase the possibility of applying image-based approaches using a generalized model, independent of the cultivar.

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

“…Arinto). Furthermore, although vBe presented a very high correlation coefficient with vBA (r = 0.82), they have been proven to not be redundant when used to estimate bunch weight [25,45]. These variables were not used singularly in the present work.…”

“…The bunch compactness trait has been explored, considering its variability between cultivars and its importance regarding grape quality and bunch health [47,48]. Our research team also demonstrated that bunch morphology is a key feature when attempting to achieve a multicultivar bunch weight estimation model [45]. Furthermore, we also proved that when converting bunch image features into weight, several image-based traits (e.g., visible berries, bunch area, average berry size) from one single bunch image are not redundant and present better results than when using single predictors on bunch images [25,45].…”

“…Tasks 1 to 4 were performed with the software Image J ®®® (v1.53k, National Institutes of Health, EUA). Image features were selected based on previous research [42,45].…”

“…In that paper, the authors have simplified the final step of converting TBA into weight by using a single factor of conversion, different for each studied cultivar. In the present work, TBA was considered along with other variables related to bunch weight [45]: vBP and vBe. However, these variables, unlike TBA, which already considers POR, are too dependent on bunch visibility and canopy density, which can be highly random.…”

“…After yield components recognition and estimation considering their occlusion, it is necessary to convert that information into vine yield, which is a challenging step as it can be highly cultivar-dependent [25,45]. Bunch morphology can vary greatly among cultivars and even between sites for the same cultivar [46].…”