Introduction

Vineyard yield forecast is a fundamental research subject in viticulture today. If done early in the season it can bring many advantages such as knowing in advance the amount of machinery and manpower needed for harvest, allocating cellar space and equipment, managing wine stock and grape prices as well as better developing marketing strategies (Dunn and Martin, 2004). However, predicting grapevine yield is very challenging because, in general, it presents a high inter-annual and spatial variability due to the effects of several factors like soil and climate conditions, grapevine variety, biotic and abiotic stresses, vineyard management practices, among others (Clingeleffer, 2001; Bramley and Hamilton, 2004; Taylor et al., 2018).

Several methods have been proposed for vineyard yield estimation. For them to be effectively advantageous for the grower, their relative error should range between 5&nbsp% (Whitty et al., 2017) and 10&nbsp% (Carrillo et al., 2016) of the actual yield. At the regional level, agroclimatic models (Fraga and Santos, 2017; Sirsat et al., 2019) and aeropalynological methods (Besselat, 1987; Cunha et al., 2016), have been explored with relative success. The last, based on grapevine pollen, is widely used across the world, however, such methods are limited to large areas or regions because climatic data has a small spatial resolution and pollen grains transported by the wind can come from a highly unpredictable range of places and distances, not being site-specific. At the vineyard level, Tarara et al. (2014) proposed the use of tension sensors connected to the trellis for measuring the tension caused by the increasing bunch weight. The obtained results presented strong linear relationships with final yield. However, this method needs a high density of sensors and a high quality and well-maintained supporting wire infrastructure.

Regardless of alternative methods, yield estimation is still widely performed in conventional ways, based on the manual sampling of yield components (YC) from vine segments in the field (Whitty et al., 2017). The YC are determined in both the previous and the current season (Tassie and Freeman, 1992) and, when multiplied together they compute the total yield of a single vine, as shown in Equation&nbsp1 (Coombe and Dry, 2001). The yield per ha is the product of the yield per vine with planting density.

$\mathrm{Y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}\mathrm{ }\left(\frac{\mathrm{k}\mathrm{g}}{\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{e}}\right)=\frac{\mathrm{ }\mathrm{n}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{s}}{\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{e}}\times \frac{\mathrm{s}\mathrm{h}\mathrm{o}\mathrm{o}\mathrm{t}\mathrm{s}}{\mathrm{n}\mathrm{o}\mathrm{d}\mathrm{e}}\times \frac{\mathrm{b}\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{s}}{\mathrm{s}\mathrm{h}\mathrm{o}\mathrm{o}\mathrm{t}}\times \frac{\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{b}\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{h}}\times \mathrm{b}\mathrm{e}\mathrm{r}\mathrm{r}\mathrm{y}\mathrm{ }\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{ }\left(\mathrm{k}\mathrm{g}\right)$
(Eq. 1)

The number of shoots per vine is largely determined by the number of buds left at winter pruning. The relationship between the number of buds and shoots with yield is highly variable and dependent on vineyard management and variety. The number of bunches per shoot is defined by the number of inflorescences initiated in the buds during the previous growing season (Vasconcelos et al., 2009). Berry number depends on the percentage of flowers that set into fruit (Keller, 2010). After fruit set, berries start to grow until a lag phase, just before veraison stage (BBCH stage 81, Lorenz et al., 1995), and then again until harvest (Coombe and McCarthy, 2000). This growth is largely dependent on climatic conditions and water availability (Ojeda et al., 2001). The final mass of each bunch is determined by the number of berries and their individual mass. If the harvest is performed manually then the rachis weight should also be considered. The number of bunches and berries alone explains about 60 % and 30 % of seasonal yield variation, respectively (Clingeleffer, 2001).

Although some of the above mentioned YC are only defined at more advanced phenological stages, yield predictions can be performed at earlier stages, if historical data is used to fulfil the remaining variables of the equation. As an example, a yield prediction can be made at budburst by knowing the real number of buds and shoots and multiplying them with previous season’s average number of bunches per shoot, berries per bunch and berry weight. Early forecasts can help vineyard managers to timely adapt their strategies to adjust their production in that season, for example by planning bunch thinning with reliable information (Dunn and Martin, 2004). However, a forecast close to harvest has a smaller chance of being inaccurate due to the negative effects of climatic events and other biotic and abiotic stresses, as the time window for them to occur is smaller. Furthermore, the closer the forecast is to harvest, the lesser need for historical data to be used, as grapes reach their full development.

Classical yield estimates are often done around berry lag phase (Clingeleffer, 2001), which can occur between 1 to 2 months before harvest. Such methods involve collecting samples of bunches from vine segments and weighing them to extrapolate the information across the whole vineyard. Then, bunch weight at harvest is estimated using Equation 2 (Clingeleffer, 2001).

Weight/bunch_(harvest) = weight/bunch_(veraison) x weight gain (Eq. 2)

This equation requires an extra variable (weight gain) which is defined as the ratio of the average bunch weight at harvest (historical data) to the average bunch weight at lag-phase. The simplicity of this method makes it achievable by any producer and is still today one of the most used in commercial vineyards (Hacking et al., 2019). However, this methodology, besides being costly and labour-dependent, its accuracy is very dependent on the weight gain factor, the number of samples and the analyzed area (larger vineyards require more samples). In many cases, growers and vineyard managers are not willing to commit enough resources for statistically correct sampling, and yield estimations are often inaccurate (Dunn and Martin, 2004). Today, new sampling strategies are often applied which can increase the quality of this method's results (Araya-Alman et al., 2019; Uribeetxebarria et al., 2019), however, they still do not completely account for its destructive and laborious problems.

With the recent development of image analysis technologies, there has been a big research effort in developing proximal sensor-based methodologies to address the challenge of yield estimation. These methods can potentially assess large areas with high image resolution, non-destructively and with georeferenced output (Gatti et al., 2016). Such systems acquire large amounts of data, mostly in the format of digital images, which need to be thoroughly processed. This is today possible due to the development of powerful image analysis systems that can automatically recognize objects in images using digital image processing and machine learning methods (Cristianini and Shawe-Taylor, 2000; Jensen, 1996; Lecun et al., 2015). In several of these works, YC such as shoots (Liu et al., 2017), flowers (Liu et al., 2018; Millan et al., 2017), bunches (Dunn and Martin, 2004; Reis et al., 2012) or berries (Aquino et al., 2017; Nuske et al., 2011) were detected automatically, while others search for image traits (bunch projected area, bunch volume, etc.) that can be used as proxies to estimate YC such as bunch weight (Hacking et al., 2019). These modern methodologies are feasible and becoming increasingly accurate at detecting yield components (Aquino et al., 2018a, Di Gennaro et al., 2019, Nuske et al., 2014; Seng et al., 2018).

Just like classical manual methods, proximal image-based yield estimation also relies on counting YC and can be performed at different phenological stages. However, image-based methods are dependent on the visibility of these YC, especially when performed non-invasively, as YC are commonly less exposed to the sensors. YC visibility can be influenced by many factors such as the size and quantity of the YC, the canopy development (nº shoots/m, leaf area/shoot), the position of the YC in the canopy and its colour and shape. These factors are largely influenced by the grape variety (vigour, internode length, leaf size, bunch size and compactness, yield potential), the training system and the phenological stage.

A very early yield forecast was attempted by Liu et al. (2017) where yield maps were computed based on automatic shoot counts from ground-based grapevine images. Images were collected between the beginning of budburst and two leaves unfolded (BBCH phenological stages 7 and 12, respectively) phases when the shoots visibility was highest. Other variables, besides the ones mentioned in equation&nbsp1, were used to turn the number of recorded shoots into yield, including the proportion of rachis weight per bunch and the harvester efficiency factor. The authors have obtained a very early yield prediction with an error ranging from 1 % (Shiraz plot) to 36 % (Chardonnay plot).

Other works approach yield estimation similarly but at more advanced phenological stages and a smaller scale. Liu et al. (2018) and Tello et al. (2019) developed automatic algorithms that successfully detect the number of flowers per inflorescence. Because of the small size of this YC, they had to collect the images in close range (~50 cm distance between the camera and the YC) and with black cardboard in the background while holding the inflorescence. Such methods need further development to be performed in a swift image acquisition manner if to be applied in a fully automatic system at a vineyard scale. Rudolph et al. (2019) collected high definition images at vine level (1 m range from the grapevine) and applied machine learning algorithms to automatically detect the number of visible inflorescences and the number of flowers in each inflorescence. Flowers were detected with high precision within the previously detected inflorescences, however, the authors underlined problems with the occlusion of some inflorescences by vegetation. Abdelghafour et al. (2019) also performed inflorescence detection at close range with high accuracy. The authors used a mobile ground platform for image collection and a Bayesian framework with a colour based pixel-wise algorithm for image classification. In these works, the authors focused on evaluating the detecting algorithm and did not perform yield estimation, however it serves as an important first step towards that goal.

Regarding berries, this YC is possible to detect right after fruit set. However, without high-resolution images, it can be challenging to detect berries at this initial stage because they are too small. As berries grow, their detection can get easier, however as they become larger, they also start occluding neighbouring berries, thus diminishing their own visibility. Similarly to the works on flowers, Aquino&nbspet&nbspal. (2018b) developed a smartphone application for counting visible berries on single-bunch images using a homogeneous background. With similar technology, Aquino&nbspet&nbspal. (2018a) performed berry counting on-field images, at a vine level, and performed accurate yield estimates on defoliated vines. This is similar to what was previously achieved by Nuske et al. (2014) but performed at earlier stages (between pea-sized berries and berry touch).

Berry detection has been reported as being easier to achieve automatically than bunch detection because berries have a less variable size and are clustered together (Zabawa et al., 2019). However, considering that bunch number is a YC that explains a higher percentage of yield variation (Clingeleffer, 2001; Pérez-Zavala et al., 2018), several researchers tried to use image analysis for general grape pixel detection and full bunch segmentation (Luo et al., 2016, 2018; Milella et al., 2019; Škrabánek and Majerík, 2017; Śkrabánek, 2018; Xiong et al., 2018). When doing so, to perform yield estimation, segmented bunches (in pixels) have to be converted into weight (Lopes et al., 2016). Hacking et al. (2019), attempted to estimate bunch weight from bunch projected area and volume, with 2D and 3D images, respectively. The experiment was performed at the bunch and vine level, in both lab and field conditions. Bunch volume was reported as the best explanatory variable of bunch weight on single bunch images, in lab conditions (full bunch visibility). However, when the same approach was attempted in field conditions, results showed that bunch projected area in 2D images outperformed the 3D alternative.

Regardless of the YC, which is intended to be detected, YC visibility is also dependent on the image collecting system and methodology. Some of the researchers reported above (e.g., Hacking et al., 2019; Nuske et al., 2011; Rudolph et al., 2019; Škrabánek and Majerík, 2017) based their image collection methods on static systems in field or lab conditions. Other works used moving platforms, (Diago et al., 2019; Kicherer et al., 2015; Lopes et al., 2017; Millan et al., 2019; Zabawa et al., 2019). In all cases, when images were collected at a vine level and in non-disturbed canopies (not defoliated), authors mentioned YC occlusion, mainly caused by leaves, as the main challenge for yield estimation (Aquino et al., 2018b; Hacking et al., 2019; Nuske et al., 2014; Rudolph et al., 2019). Only in the case of shoot detection, when the grapevine vegetative development is still at an early stage, is this occlusion not stated as a problem. To increase YC exposure, authors that performed yield estimation at later stages opted for partial or total leaf removal prior to image acquisition. However, although leaf removal is generally used in cool climate viticulture, in warmer regions this technique has to be performed with caution as high temperatures can induce berry sunburn, damaging grapes and decreasing yield and must quality (Krasnow et al., 2010).

The main objective of this work is to contribute to the decision regarding which variables to look for when approaching grapevine yield estimation using image analysis with a ground platform. For that, two specific goals were set. Firstly, to study the magnitude of YC occlusion in natural vineyard conditions to evaluate the degree of visibility of each YC throughout the growing cycle. Secondly, to explore image-based attributes, after berry set, to study the best predictors of bunch weight in single bunch images and of yield in non-defoliated grapevine field images.

Material and methods

2. Image acquisition and processing

For the online data collection, a robot platform developed on the frame of the EU project Vinbot (http://www.vinbot.eu/) was used. The robot carries an RGB-D Kinect v2 camera (Kinect v2.0, Microsoft) and two 2D laser range finders, one for navigation and one to obtain data regarding canopy shape (Guzman et al., 2016; Lopes et al., 2017). All images were collected from non-manipulated vines using the robot's camera, mounted in a lateral perspective (Figure 1). Images were taken of the sunlit side of the canopy, at a distance varying from 0.70 to 1.0&nbspm. To collect images from the fruiting zone, the robot camera was positioned at approximately 1 m above the ground.

Figure 1. View of the Vinbot platform in action in field conditions.

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The vines were imaged at the following seven phenological stages: just before budburst (BBCH 00, ~6&nbspmonths before harvest), between bud burst and four leaves unfolded (BBCH&nbsp14, ~5&nbspmonths before harvest), inflorescences visible (BBCH&nbsp53, ~4&nbspmonths before harvest), pre-flowering (BBCH&nbsp57, ~3&nbspmonths before harvest), pea-sized berries (BBCH&nbsp75, ~2&nbspmonths before harvest), veraison (BBCH&nbsp81, ~1&nbspmonths before harvest) and harvest (BBCH&nbsp89) (Figure&nbsp2). A fixed plastic bar was positioned under the canopy all along the smart points and used as a reference scale. Each image encompassed a canopy segment of 1&nbspm (~1&nbspvine, Figure&nbsp3) and was analyzed individually.

Figure 2. BBCH phenological stages used for data collection (e.g., of variety Encruzado); A) dormancy: BBCH 00; B) 4 leaves unfolded: BBCH 14; C) inflorescences visible: BBCH 53; D) pre-flowering: BBCH 57; E) pea-sized berries: BBCH 75; F) veraison: BBCH 81; G) harvest: BBCH 89.

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The number of spurs and yield components were manually assessed in each image (estimated data) at each phenological stage. Furthermore, at pea-sized berries, veraison and harvest stages, the total bunch projected area per vine segment was computed. Ground-truth (observed data) was assessed visually in the field at the same phenological stages—YCs were manually determined for each of the canopy segments previously imaged. At harvest, all bunches per vine segment were picked, counted and weighed, per meter. As spurs had about the same number of buds (2 buds/spur), for simplicity the variable number of spurs was analyzed as a yield component as it is a proxy of the number of nodes per vine.

At pea size, veraison and harvest, after data collection in the field, a sample of ~80 bunches per variety was collected from outside of the smart points and taken into lab conditions for further assessments. In the lab, two images were collected per bunch, in perpendicular perspectives, using a standard commercial camera (Nikon D5200) (Figure 4A). These images were then used to compute the projected area, perimeter and maximum length of each bunch. Total bunch weight was measured using a table scale (KERN FCB v1.4). Bunch volume was acquired using the water displacement method. Berries were then destemmed and placed separately on a table for image collection (Figure&nbsp4B). ImageJ’s built-in option for analyzing particles was then used to automatically count the number of berries. Furthermore, at the same phenological stages, bunch contours in the images were manually labelled using a standard labelling software (ImageJ®) to estimate the field visible bunch area.

Figure 4. Lab images of a bunch (A) and berries (B) of the variety ‘Encruzado’ acquired at the beginning of veraison.

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Results

Discussion

Conclusion

In this work, the visibility of several yield components was explored in field conditions along the grapevine growing cycle. Furthermore, several image-based bunch attributes were explored in lab and field conditions aiming at finding the best predictors for yield estimation. Regarding yield components’ visibility, at dormancy and early shoot growth stages, all varieties showed good visibility of spurs and shoots. Although these components did not show significant correlation coefficients with final yield (except for Syrah), results still suggest that they could be used as auxiliary variables in later estimation algorithms. The number of shoots was highly and significantly correlated with yield only for the variety Syrah, indicating that shoot number might be too dependent on the variety and vine management to be used as the only predictor of yield at budburst.

Both inflorescence and bunch count occlusion rates reached high values well above 50&nbsp%, thus other variables should be considered as proxies of final yield besides their counts. Bunch volume and bunch projected area had the highest correlation coefficients with yield in lab conditions, being bunch area the easiest variable to obtain. In field conditions, bunch area occlusion presented high results (above 60&nbsp%) at all studied phenological stages, similar to what happened with bunch counts. Lower occlusion values were observed for mild water-stressed Syrah vines, which presented some leaf senescence near harvest. Regardless of these high occlusion rates, the visible bunch projected area of non-manipulated grapevine canopies showed significant determination coefficients with yield, proving that, even in dense canopies, bunch area remains a promising explanatory variable of vine yield. Further work is needed to explore these relations in other conditions, as YC visibility can vary widely with vineyard characteristics. Our current research efforts are directed towards performing grapevine yield estimations using visible bunch projected area and other auxiliary variables related to vegetative and reproductive traits that can help to estimate the portion of occluded yield components.

Acknowledgements

Gonçalo Victorino was supported by the PhD grant SFRH/BD/132305/2017, sponsored by FCT—Fundação para a Ciência e a Tecnologia. The robot used in this research was developed within the Project VINBOT, funded by the European Community’s Seventh Framework Program (SME 2013-2, grant agreement nº 605630). The authors would like to acknowledge all the master students from Instituto Superior de Agronomia that helped with the data collection.

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