A Method to estimate Grape Phenolic Maturity based on Color Features

The phenolic ripeness of the grape is one of the most important parameters to determine the optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a innovative method to estimate the Grape Phenolic Maturity based in digital images is presented. Three classes of seed are de ned (immature, mature and overmature) by the expert (enologist) involved in the research. A robust method of segmentation was proposed. The classi cation of seeds according to their degree of maturity was performed by a Arti cial Neural Network. Descriptor used by the Neural Networks corresponds to a histogram of the occur- rence of colors in a color scale. The method as a whole proved to be simple and e ffective in the classi ffication of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions due the high performance obtained.Eje: XII Workshop de Computación gráfica, Imágenes y VisualizaciónRed de Universidades con Carreras de Informática (RedUNCI

A Method to estimate Grape Phenolic Maturity based on Color Features

The phenolic ripeness of the grape is one of the most important parameters to determine the optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a innovative method to estimate the Grape Phenolic Maturity based in digital images is presented. Three classes of seed are de ned (immature, mature and overmature) by the expert (enologist) involved in the research. A robust method of segmentation was proposed. The classi cation of seeds according to their degree of maturity was performed by a Arti cial Neural Network. Descriptor used by the Neural Networks corresponds to a histogram of the occur- rence of colors in a color scale. The method as a whole proved to be simple and e ffective in the classi ffication of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions due the high performance obtained.Eje: XII Workshop de Computación gráfica, Imágenes y VisualizaciónRed de Universidades con Carreras de Informática (RedUNCI

Fruit Maturity Estimation based on Color Scales

Color is an important parameter used to estimate fruit maturity and optimal harvest time. In this paper a general methodology for estimating fruit maturity based on the development of a color scale is proposed. Maturity estimation is performed by computing of the fruit representative color and its comparison with the colors of the scale. In experimentation the case of grape maturity estimation based on seed color is presented. The method is simple and eff ective, allowing its application in the real processes.Eje: XII Workshop de Computación gráfica, Imágenes y VisualizaciónRed de Universidades con Carreras de Informática (RedUNCI

An Analysis of Seed Colour During Ripening of Cabernet Sauvignon Grapes

This case study examines seed colour during ripening using an exhaustive colorimetric analysis. The aim was to associate the chemical ripening with the seed colour in order to estimate the ripening stages of Cabernet Sauvignon grapes. Cluster samples, obtained from a vertical shoot-positioned vineyard in central Chile, were monitored for their technological and phenolic maturity, as well as for the colorimetric and chemical characteristics of the seeds. The colours of the scanned seed images were determined by human as well as by computer vision. In the first case, an expert assigned a colour to each seed part. In the second case, a computer program estimated the colours of the scanned seed images. An exhaustive analysis of seed colour was proposed, instead of a general observation of seed browning. The seed colour presented a wide range of colours, from moss green to dark brown, depending on the maturity and the face observed. The ripening stages identified, along with the chemical and colorimetric information gathered, were under ripe seed (brown with green traces), ripe seed (dark brown with green traces) and overripe seed (dark brown without any green traces). A new way to quantify seed colour is shown in this paper

A Method to estimate Grape Phenolic Maturity based on Color Features

The phenolic ripeness of the grape is one of the most important parameters to determine the optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a innovative method to estimate the Grape Phenolic Maturity based in digital images is presented. Three classes of seed are de ned (immature, mature and overmature) by the expert (enologist) involved in the research. A robust method of segmentation was proposed. The classi cation of seeds according to their degree of maturity was performed by a Arti cial Neural Network. Descriptor used by the Neural Networks corresponds to a histogram of the occur- rence of colors in a color scale. The method as a whole proved to be simple and e ffective in the classi ffication of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions due the high performance obtained.Eje: XII Workshop de Computación gráfica, Imágenes y VisualizaciónRed de Universidades con Carreras de Informática (RedUNCI

Detection of Grape Clusters in Images using Convolutional Neural Network

Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest and sort technique where the foreign objects are separated later from grapes after harvesting them using an automatic harvester. Currently available systems are trained on data that is from developed or grape-producing countries, thus showing data biases when used at any new location thus it gives rise to a need of creating a dataset from scratch to verify the results of research. Grape is available in different sizes, colors, seed sizes, and shapes which makes its detection, through simple Computer vision, even more challenging. This research addresses this issue by bringing the solution to this problem by using CNN and Neural Networks using the newly created dataset from local farms as the other research and the methods used don’t address issues faced locally by the farmers. YOLO has been selected to be trained on the locally collected dataset of grapes

Fruit Maturity Estimation based on Color Scales

Color is an important parameter used to estimate fruit maturity and optimal harvest time. In this paper a general methodology for estimating fruit maturity based on the development of a color scale is proposed. Maturity estimation is performed by computing of the fruit representative color and its comparison with the colors of the scale. In experimentation the case of grape maturity estimation based on seed color is presented. The method is simple and eff ective, allowing its application in the real processes.Eje: XII Workshop de Computación gráfica, Imágenes y VisualizaciónRed de Universidades con Carreras de Informática (RedUNCI

Hyperspectral signatures and reflectance models related to the ripening index in four grape varieties

The preference for the consumption of red wine in Mexico is increasing because its components derived from the grape are attributed to health benefits. The quality of wine depends mostly on the vineyard conditions. The objective of this study was able to differentiate the physicochemical composition in the harvest stage of four varieties of red grapes that are used in the production of wine to relate their maturation with those of their hyperspectral signatures. Various parameters including pH, total soluble solids, color, weight, and morphology were determined from the bunches of grapes. Concerning the maturity index, it was observed that the grapes with the highest degree of maturity were Shiraz and Merlot at harvest time. The pH of grape juice is a measure of active acidity; the texture is considered a quick and inexpensive technique. The hyperspectral signatures reflectances versus color, total soluble solids, morphology, weight, texture, and pH for each grape variety was best fitted with Gaussian curves of order 8 to Cabernet sauvignon and Merlot, 7 to Malbec, and 5 to Shiraz with R2 above 0.99

From the Laboratory to The Vineyard—Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis

Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectralimaging)provideanalystswithaninnovativeandimprovedunderstandingofcomplex issuesbydeterminingseveralchemicalcompoundsandmetabolitesatonce,allowingforthecollection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition

From the Laboratory to The Vineyard—Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis

Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectralimaging)provideanalystswithaninnovativeandimprovedunderstandingofcomplex issuesbydeterminingseveralchemicalcompoundsandmetabolitesatonce,allowingforthecollection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition