Unsupervised Learning for Ripeness Estimation From Grape Seeds Images

Hernández, S.; Morales, Libby; Urrutia, Angélica

doi:10.21307/ijssis-2017-225

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…In [19], a method for ripeness estimation based on grape seed images was introduced by Hernandez et al The authors proposed a Dirichlet mixture (DMM) as a generative model for clustering grape seeds. The DMM model directly used the color histograms of RGB and HSV color spaces to probabilistically assign the grape seed images to different clusters (two and three classes were tested) using a cluster membership indicator.…”

Section: Related Researchmentioning

confidence: 99%

A Non-Destructive Method for Grape Ripeness Estimation Using Intervals’ Numbers (INs) Techniques

et al. 2022

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Grape harvesting based on estimated in-field maturity indices can reduce the costs of pre-harvest exhaustive sampling and chemical analysis, as well as the costs of post-harvest storage and waste across the production chain due to the non-climacteric nature of grapes, meaning that they are not able to reach desired maturity levels after being removed from the vine. Color imaging is used extensively for intact maturity estimation of fruits. In this study, color imaging is combined with Intervals’ Numbers (INs) technique to associate grape cluster images to maturity-related indices such as the total soluble solids (TSSs), titratable acidity (TA), and pH. A neural network regressor is employed to estimate the three indices for a given input of an IN representation of CIELAB color space. The model is tested on one hundred Tempranillo cultivar images, and the mean-square error (MSE) is calculated for the performance evaluation of the model. Results reveal the potential use of the Ins’ NN regressor for TSS, TA, and pH assessment as a non-destructive, efficient, fast, and cost-effective tool able to be integrated into an autonomous harvesting robot.

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Section: Related Researchmentioning

confidence: 99%

A Non-Destructive Method for Grape Ripeness Estimation Using Intervals’ Numbers (INs) Techniques

et al. 2022

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“…In [26], visual inspection of grape seeds took place for grape ripening estimation by the Dirichlet Mixture Model (DMM), without the performance of chemical analyses. DMM allowed modeling the color histogram of grape seeds to estimate ripening class memberships.…”

Section: Color Imagingmentioning

confidence: 99%

“…Intact/On-Site Estimation Limitations/Review [15] No/No Applied to grape seeds in an in-lab closed illumination box with a digital camera, illumination-dependent [14] No/No Applied to grape seeds and grape berries in an in-lab illumination box with a digital camera, illumination-dependent [22] Yes/Yes Applied to grape bunches on-site, fails occasionally due to segmentation algorithm setup of berries circle radius and circle detection algorithm [23] No/No Applied to grape seeds and berries in an in-lab set [24] Yes/Yes Applied to grape bunches, camera system mounted on a vehicle [25] No/No Applied to grape berries, cost-effective in-lab setup [26] No/No Applied to grape seeds, in-lab, depends only on color histograms [27] Yes/No Applied to grape bunches, in-lab set, on a black background, under eight halogen lamps [28] Yes/Yes Applied to grape bunches on site by using a smartphone camera [29] Yes/Yes Applied to grape bunches on site, pilot study where only the green color channel histograms were selected and post-processed [30] No/No Applied to grape berries, in-lab inside a dark chamber, with 15 3W LED red, green, blue, warm white, and cool white illuminants [31] No/No Applied to removed grape berries in an in-lab dark room, use of costly hyperspectral imaging system [32] Yes/No Applied to grape bunch in-lab inside a dark room under blue reflector lamps, only six berries as samples from each bunch [33] Yes/No Applied to grape bunch in-lab dark room under blue reflector lamps, only six berries as samples from each bunch, low generalization ability [34] Yes/Yes Farm scale, based on a hypothesis on carotenoid content [35] No/No Applied to grape skins and seeds, under an illumination unit of four tungsten halogen lamps [36] No/No Applied to grape seeds, in-lab under iodine halogen lamps [37] Yes/Yes Applied to grape bunches on-site, using images acquired by a motorized platform [38] No/No Applied to grape berries in a box under a quartz tungsten halogen lighting unit [13] No/No Applied to grape berries in-lab under illumination source…”

Section: Refmentioning

confidence: 99%

Machine Vision for Ripeness Estimation in Viticulture Automation

et al. 2021

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Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined.

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“…To estimate grape maturity, color images have been utilized and processed using standard machine learning algorithms, such as artificial neural networks [28,29], random forests [30], unsupervised clustering [31], and CNNs [32][33][34][35]. For ground-truth maturity levels, some methods in the literature have utilized grape color and shape information [28,30,31,36], the knowledge of the harvesting week [33], and well-known chemical indices, such as total soluble solids (TSSs), titratable acidity (TA), and pH [29]. Attention mechanisms have also been employed, achieving significant performance improvements in grape maturity estimation.…”

Section: Introductionmentioning

confidence: 99%

A Grape Dataset for Instance Segmentation and Maturity Estimation

Blekos,

Chatzis,

Kotaidou

et al. 2023

Agronomy

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Grape maturity estimation is vital in precise agriculture as it enables informed decision making for disease control, harvest timing, grape quality, and quantity assurance. Despite its importance, there are few large publicly available datasets that can be used to train accurate and robust grape segmentation and maturity estimation algorithms. To this end, this work proposes the CERTH grape dataset, a new sizeable dataset that is designed explicitly for evaluating deep learning algorithms in grape segmentation and maturity estimation. The proposed dataset is one of the largest currently available grape datasets in the literature, consisting of around 2500 images and almost 10 k grape bunches, annotated with masks and maturity levels. The images in the dataset were captured under various illumination conditions and viewing angles and with significant occlusions between grape bunches and leaves, making it a valuable resource for the research community. Thorough experiments were conducted using a plethora of general object detection methods to provide a baseline for the future development of accurate and robust grape segmentation and maturity estimation algorithms that can significantly advance research in the field of viticulture.

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Unsupervised Learning for Ripeness Estimation From Grape Seeds Images

Cited by 4 publications

References 18 publications

A Non-Destructive Method for Grape Ripeness Estimation Using Intervals’ Numbers (INs) Techniques

A Non-Destructive Method for Grape Ripeness Estimation Using Intervals’ Numbers (INs) Techniques

Machine Vision for Ripeness Estimation in Viticulture Automation

A Grape Dataset for Instance Segmentation and Maturity Estimation

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