Trellis Tension Monitoring Improves Yield Estimation in Vineyards

Blom, Paul E.; Tarara, Julie M.

doi:10.21273/hortsci.44.3.678

Cited by 20 publications

(13 citation statements)

References 23 publications

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“…Some authors report errors in yield predictions of 10-15% (Miranda and Royo, 2004;Tarara et al, 2005;Nuske et al, 2011). Yet, others report errors of 20% (Blom and Tarara, 2009) and 30% (Dunn, 2010). The present models were more accurate, especially V1 and V2 -despite being of use relatively early in the growth cycle -and can be considered statistically acceptable since they meet the criteria of Power (1993), i.e., they show no significant predictive bias, adequate accuracy, and the prediction residuals are normally distributed.…”

Section: Discussionmentioning

confidence: 99%

Comparison of different methods of grapevine yield prediction in the time window between fruitset and veraison

Fuente

Linares

Baeza³

et al. 2015

OENO One

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Aim: To compare grape yield prediction methods to determine which provide the best results in terms of earliness of prediction in the growing season, accuracy and precision.Methods and results: The grape yields predicted by six models – one for use at fruitset (FS), two for use at veraison (V1 and V2), and three for use during the lag phase (LP40, LP50 and LP60) – were compared to field-measured yields. Regressions for the yield predicted by each model were constructed. The V1 and V2 models had the highest R2 (0.75) and efficiency index (EF; 0.67-0.71) and the lowest RMSE values (±16-17%, or <0.5 kg per m of row). The FS model had the same or similar R2 (0.58), EF (0.06) and RMSE (±30%, or <0.83 kg per m of row) values as the LP models, but allowed yield predictions to be made one month earlier.Conclusion: The validated FS, V1 and V2 models are all useful in predicting grape yields and could be used to accurately forecast (with different errors) grape yields at either early or later time points according to winery needs. These models could be improved as further data become available in following seasons.Significance and impact of the study: Few validated models are available for predicting grapevine yields at fruitset and veraison. This study provides predictive models that can be used at these different times of the growth cycle.

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Section: Discussionmentioning

confidence: 99%

Comparison of different methods of grapevine yield prediction in the time window between fruitset and veraison

Fuente

Linares

Baeza³

et al. 2015

OENO One

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“…Sensor‐based technologies using trellis tension monitors, multispectral sensors, terahertz‐wave imaging, and visible‐light image processing have all been proposed for yield estimation in vineyards. A dynamic yield estimation system based on trellis tension monitors has been demonstrated (Blom & Tarara, ), but it requires a permanent infrastructure to be installed. Further, variation in yield is not the only cause of variations in trellis tension.…”

Section: Related Workmentioning

confidence: 99%

Automated Visual Yield Estimation in Vineyards

Nuske

Wilshusen

Achar

et al. 2014

Journal of Field Robotics

208

179

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We present a vision system that automatically predicts yield in vineyards accurately and with high resolution. Yield estimation traditionally requires tedious hand measurement, which is destructive, sparse in sampling, and inaccurate. Our method is efficient, high‐resolution, and it is the first such system evaluated in realistic experimentation over several years and hundreds of vines spread over several acres of different vineyards. Other existing research is limited to small test sets of 10 vines or less, or just isolated grape clusters, with tightly controlled image acquisition and with artificially induced yield distributions. The system incorporates cameras and illumination mounted on a vehicle driving through the vineyard. We process images by exploiting the three prominent visual cues of texture, color, and shape into a strong classifier that detects berries even when they are of similar color to the vine leaves. We introduce methods to maximize the spatial and the overall accuracy of the yield estimates by optimizing the relationship between image measurements and yield. Our experimentation is conducted over four growing seasons in several wine and table‐grape vineyards. These are the first such results from experimentation that is sufficiently sized for fair evaluation against true yield variation and real‐world imaging conditions from a moving vehicle. Analysis of the results demonstrates yield estimates that capture up to 75% of spatial yield variance and with an average error between 3% and 11% of total yield.

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“…This is the case of image analysis that has been proposed to detect, count and estimate the weight of clusters (Diago et al 2012;Reis et al 2012;Nuske et al 2011;Serrano et al 2005;Dunn and Martin 2004) or to estimate the number and the volume of berries (Grocholsky et al 2011;Rabatel and Guizard 2007). Other authors have proposed continuous weighing devices positioned on the trellising system (Blom and Tarara 2009) at a specific location in the field. These approaches aim to facilitate the work of observers and possibly reduce the estimation error by increasing n, the number of samples measured while maintaining or reducing the time required to make field observations.…”

Section: Introductionmentioning

confidence: 99%

Use of multi-spectral airborne imagery to improve yield sampling in viticulture

et al. 2015

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The wine industry needs to know the yield of each vine field precisely to optimize quality management and limit the costs of harvest operations. Yield estimation is usually based on random vine sampling. The resulting estimations are often not precise enough because of the high variability within vineyard fields. The aim of the work was to study the relevance of using NDVI-based sampling strategies to improve estimation of mean field yield. The study was conducted in nine non-irrigated vine fields located in southern France. For each field, NDVI was derived from multi-spectral airborne images. The variables which define the yield: [berry weight at harvest (BWh), bunch number per vine (BuN) and berry number per bunch (BN)] were measured on a regular grid. This database allowed for five different sampling schemes to be tested. These sampling methods were mainly based on a stratification of NDVI values, they differed in the way as to whether NDVI was used as ancillary information to design a sampling strategy for BuN, BN, BW or for all yield variables together. Results showed a significant linear relationship between NDVI and BW, indicating the interest of using NDVI information to optimize sampling for this parameter. However this result is mitigated by the low incidence of BW in the yield variance (4 %) within the field. Other yield components, BuN and BN explain a higher percentage of yield variance (60 and 11 % respectively) but did not show any clear relationship with NDVI. A large difference was observed between fields, which justifies testing the optimized sampling methods on all of them and for all yield variables. On average, sampling methods based on NDVI systematically improved vine field yield estimates by at least 5-7 % compared to the random method. Depending on the fields, error improvement ranged from -2 to 15 %. Based on these results, the practical & B. Tisseyre recommendation is to consider a two-step sampling method where BuN is randomly sampled and BW is sampled according to the NDVI values.

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Trellis Tension Monitoring Improves Yield Estimation in Vineyards

Cited by 20 publications

References 23 publications

Comparison of different methods of grapevine yield prediction in the time window between fruitset and veraison

Comparison of different methods of grapevine yield prediction in the time window between fruitset and veraison

Automated Visual Yield Estimation in Vineyards

Use of multi-spectral airborne imagery to improve yield sampling in viticulture

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