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

Abstract

<p style="text-align: justify;"><strong>Aim</strong>: 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.</p><p style="text-align: justify;"><strong>Methods and results</strong>: The grape yields predicted by six models – one for use at fruitset (FS), two for use at <em>veraison</em> (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 R<sup>2</sup> (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 R<sup>2</sup> (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.</p><p style="text-align: justify;"><strong>Conclusion</strong>: 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.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: Few validated models are available for predicting grapevine yields at fruitset and <em>veraison</em>. This study provides predictive models that can be used at these different times of the growth cycle.<strong></strong></p>

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

“…Our findings are of great significance, because, traditionally, there is no reliable approach for predicting berry yield and quality before harvest [111][112][113][114]. Here we recommend that the use of hyperspectral sensors, especially imaging hyperspectral sensors mounted on UAVs, will be faster and more computationally inexpensive compared to traditional methods.…”

Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation

“…Unfortunately, the two-step method is labour-intensive, error-prone and destructive in the estimation process. Additionally, De la Fuente et al [7] presented yield prediction models using destructive, manually collected data between fruit-set and vèraison, aligning with the more ‘classical’ two-step method. To overcome the limitations of the manual methods, modern techniques have employed sensors attached to automatic harvesters to monitor yield during the harvesting process [3].…”

Yield of Müller-Thurgau and Riesling grapevines is altered by meteorological conditions in the current and previous growing seasons

“…Because inflorescence primordia are initiated in the year preceding the year of harvest, yield formation in grapevine is considered a two-year process (Guilpart et al, 2014). Consequently, grapevine reproductive behaviour is affected by the environmental conditions in both the present as well as the preceding year (De la Fuente et al, 2015).…”