![]() ![]() 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. We have verified how the prediction accuracy changes depending on the internal structure of the our network.Yield assessment is a highly relevant task for the wine industry. In the evaluation, the performance of our proposed network is investigated by focusing on clarifying the importance of each module in the network. Since both RGB and depth images are incorporated into our system, the plant growth can be represented in 3D space. Based on neural network based image translation and time-series prediction, we construct a system that gives the predicted result of RGB-D images from several past RGB-D images. We have verified how the prediction accuracy changes depending on the internal structure of the our network.ĪB - This paper presents a method to predict three-dimensional (3D) plant growth with RGB-D images. N2 - This paper presents a method to predict three-dimensional (3D) plant growth with RGB-D images. T1 - 3D plant growth prediction via image-to-image translationĪ part of this work is supported by JSPS KAKENHI Grant Number JP17H01768 and JP18H04117.Ĭopyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. ![]()
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