Moreover, the yield assessment methods reported in the literature are classified and compared in light of the measurement, estimation and modelling approaches. In this review, the most relevant features regarding yield development in an operational scenario that have to be considered by yield assessment methods are summarized and discussed. An interesting review of the approaches, methods and challenges for vineyard yield estimation, prediction and forecasting was recently published by Laurent et al. Several works on yield estimation and forecasting in vineyards have been published in the last decades. Vineyard yield prediction is crucial to achieve the desired fruit quantity and quality (Krstic et al., 1998 Taylor et al., 2019), therefore, the objective and fast estimation of vine yield would be very valuable for grapegrowers (Dunn & Martin, 2000 Laurent et al., 2021 Martin et al., 2003). Yield prediction has been recognized as a key subject in agriculture (Klompenburg et al., 2020), and particularly in the grape and wine industry (Carrillo et al., 2016 Clingeleffer et al., 2001 Dunn & Martin, 2003 Laurent et al., 2021 Taylor et al., 2019). The number of actual berries and yield per vine can be predicted up to 60 days prior to harvest in several grapevine varieties using the new algorithm. In terms of yield forecast, the correlation between the actual yield and its estimated value yielded R 2 between 0.54 and 0.87 among different varieties and NRMSE between 16.47% and 39.17% while the global model (including all varieties) had a R 2 equal to 0.83 and NRMSE of 29.77%. The method yielded average values for root mean squared error (RMSE) of 195 berries, normalized RMSE (NRMSE) of 23.83% and R 2 of 0.79 between the number of estimated and the number of actual berries per vine using the leave-one-out cross validation method. Regarding the berries’ detection step, a F1-score average of 0.72 and coefficients of determination (R 2) above 0.92 were achieved for all varieties between the number of estimated and the number of actual visible berries. ![]() All features were used to train support vector regression (SVR) models for predicting number of actual berries and yield. A SegNet architecture was employed to detect the visible berries and canopy features. Vines from six grapevine ( Vitis vinifera L.) varieties were photographed using a mobile platform in a commercial vineyard at pea-size berry stage. The goal of this work was to develop a new algorithm for early yield prediction in different grapevine varieties using computer vision and machine learning. Our experiments on two large-scale motion prediction datasets demonstrate that our model yields high-quality pose sequences that are much more diverse than those from state-of-the-art stochastic motion prediction techniques.Yield assessment is a highly relevant task for the wine industry. We exploit this idea for motion prediction by incorporating it into a recurrent encoder-decoder network with a conditional variational autoencoder block that learns to exploit the perturbations. ![]() Alternatively, in this paper, we propose to stochastically combine the root of variations with previous pose information, so as to force the model to take the noise into account. ![]() ![]() This combination, however, is done in a deterministic manner, which gives the network the flexibility to learn to ignore the random noise. Existing approaches to modeling this stochasticity typically combine a random noise vector with information about the previous poses. However, human motion is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Human motion prediction, the task of predicting future 3D human poses given a sequence of observed ones, has been mostly treated as a deterministic problem.
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