End-to-End Automatic Berry Counting for Table Grape Thinning

Buayai, Prawit; Saikaew, Kanda Runapongsa; Mao, Xiaoyang

doi:10.1109/access.2020.3048374

Cited by 27 publications

(12 citation statements)

References 38 publications

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“…Indeed, with manual detection it is possible to count all visible berries, even those that are very small or only partially visible; however, automated methods are much more prone to errors. These errors can result from several sources, like the conditions in which the images are acquired (view angle and illumination), the visible bunch fraction imaged, the algorithms used for berry counting and detection, and berry features, such as colour, diameter, circularity, density and homogeneity of distribution within the bunch (Diago et al, 2015;Aquino et al, 2017;Buayai et al, 2021;Pérez-Zavala et al, 2018).…”

Section: Number Of Visible Berriesmentioning

confidence: 99%

Grapevine bunch weight estimation using image-based features: comparing the predictive performance of number of visible berries and bunch area

Lopes

Cadima

2021

OENO One

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Recent advances in machine vision technologies have provided a multitude of automatic tools for recognition and quantitative estimation of grapevine bunch features in 2D images. However, converting them into bunch weight (BuW) is still a big challenge. This paper aims to compare the explanatory power of the number of visible berries (#vBe) and the bunch area (BuA) in 2D images, in order to predict BuW. A set of 300 bunches from four grapevine cultivars were picked at harvest and imaged using a digital RGB camera. Then each bunch was manually assessed for several morphological attributes and, from each image, the #vBe was visually assessed while BuA was segmented using manual labelling combined with an image processing software. Single and multiple regression analysis between BuW and the image-based variables were performed and the obtained regression models were subsequently validated with two independent datasets.The high goodness of fit obtained for all the linear regression models indicates that either one of the image-based variables can be used as an accurate proxy of actual bunch weight and that a general model is also suitable. The comparison of the explanatory power of the two image-based attributes for predicting bunch weight showed that the models based on the predictor #vBe had a slightly lower coefficient of determination (R2) than the models based on BuA. The combination of the two image-based explanatory variables in a multiple regression model produced predictor models with similar or noticeably higher R2 than those obtained for single-predictor models. However, adding a second variable produced a higher and more generalised gain in accuracy for the simple regression models based on the predictor #vBe than for the models based on BuA. Our results recommend the use of the models based on the two image-based variables, as they were generally more accurate and robust than the single variable models. When the gains in accuracy produced by adding a second image-based feature are small, the option of using only a single predictor can be chosen; in such a case, our results indicate that BuA would be a more accurate and less cultivar-dependent option than the #vBe.

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Section: Number Of Visible Berriesmentioning

confidence: 99%

Grapevine bunch weight estimation using image-based features: comparing the predictive performance of number of visible berries and bunch area

Lopes

Cadima

2021

OENO One

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“…Other works have also addressed the problem of estimating the number of actual berries using several features extracted from RGB images. Buayai et al (2021) estimated the number of actual berries using five features extracted from individual clusters' images in which occlusion phenomena were only due to other berries, but not to leaves or other canopy elements. In this work, the authors achieved the best performance using a random forest regression that yielded a mean absolute error of estimation of 3.79 berries per cluster.…”

Section: Number Of Estimated Visible Berries F2mentioning

confidence: 99%

“…Most of the previous works have focused on the detection of visible fruits. Some authors have developed algorithms capable of quantifying the number of visible berries in RGB images acquired under natural conditions, using traditional image analysis methods (Aquino et al, 2018;Nuske et al, 2014b) or newer deep learning techniques (Buayai et al, 2021;Grimm et al, 2019;Klompenburg et al, 2020). These works have suggested a procedure on yield forecasting for defoliated vineyards where the number of visible berries in the images was proportional to the total number of berries.…”

Section: Introductionmentioning

confidence: 99%

Early yield prediction in different grapevine varieties using computer vision and machine learning

et al. 2022

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Yield assessment is a highly relevant task for the wine industry. 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. Vines from six grapevine (Vitis vinifera L.) varieties were photographed using a mobile platform in a commercial vineyard at pea-size berry stage. A SegNet architecture was employed to detect the visible berries and canopy features. All features were used to train support vector regression (SVR) models for predicting number of actual berries and yield. Regarding the berries’ detection step, a F1-score average of 0.72 and coefficients of determination (R2) above 0.92 were achieved for all varieties between the number of estimated and the number of actual visible berries. The method yielded average values for root mean squared error (RMSE) of 195 berries, normalized RMSE (NRMSE) of 23.83% and R2 of 0.79 between the number of estimated and the number of actual berries per vine using the leave-one-out cross validation method. In terms of yield forecast, the correlation between the actual yield and its estimated value yielded R2 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 R2 equal to 0.83 and NRMSE of 29.77%. 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.

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“…Many agricultural support systems have been proposed, and all have been based on information and communication technology (ICT) and artificial intelligence (AI) [8][9][10][11][12][13]. In particular, research on the automatic detection of fruits from images has been extensive [8,11].…”

Section: Introductionmentioning

confidence: 99%

Appropriate grape color estimation based on metric learning for judging harvest timing

et al. 2022

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The color of a bunch of grapes is a very important factor when determining the appropriate time for harvesting. However, judging whether the color of the bunch is appropriate for harvesting requires experience and the result can vary by individuals. In this paper, we describe a system to support grape harvesting based on color estimation using deep learning. To estimate the color of a bunch of grapes, bunch detection, grain detection, removal of pest grains, and color estimation are required, for which deep learning-based approaches are adopted. In this study, YOLOv5, an object detection model that considers both accuracy and processing speed, is adopted for bunch detection and grain detection. For the detection of diseased grains, an autoencoder-based anomaly detection model is also employed. Since color is strongly affected by brightness, a color estimation model that is less affected by this factor is required. Accordingly, we propose multitask learning that uses metric learning. The color estimation model in this study is based on AlexNet. Metric learning was applied to train this model. Brightness is an important factor affecting the perception of color. In a practical experiment using actual grapes, we empirically selected the best three image channels from RGB and CIELAB (L*a*b*) color spaces and we found that the color estimation accuracy of the proposed multi-task model, the combination with “L” channel from L*a*b color space and “GB” from RGB color space for the grape image (represented as “LGB” color space), was 72.1%, compared to 21.1% for the model which used the normal RGB image. In addition, it was found that the proposed system was able to determine the suitability of grapes for harvesting with an accuracy of 81.6%, demonstrating the effectiveness of the proposed system.

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End-to-End Automatic Berry Counting for Table Grape Thinning

Cited by 27 publications

References 38 publications

Grapevine bunch weight estimation using image-based features: comparing the predictive performance of number of visible berries and bunch area

Grapevine bunch weight estimation using image-based features: comparing the predictive performance of number of visible berries and bunch area

Early yield prediction in different grapevine varieties using computer vision and machine learning

Appropriate grape color estimation based on metric learning for judging harvest timing

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