A Comparison of Fuzzy Clustering Algorithms Applied to Feature Extraction on Vineyard

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color image segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a large class of images. However, it is not adequate for noisy images and it also takes more time for execution as compared to other method as K-means. For this reason, several methods have been proposed to improve these weaknesses. Method like Possibilistic C-Means, Fuzzy Possibilistic C-Means, Robust Fuzzy Possibilistic C-Means and Fuzzy C-Means with Gustafson-Kessel algorithm. In this paper we perform a comparison of these clustering algorithms applied to feature extraction on vineyard images. Segmented images are evaluated using several quality parameters such as the rate of correctly classied area and runtim

Assessing Berry Number for Grapevine Yield Estimation by Image Analysis: Case Study with the Red Variety “Syrah”

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoThe yield estimation provides information that help growers to make decisions in order to optimize crop growth and to organize the harvest operations in field and in the cellar. In most vineyard estates yield is forecasted using manual methods. However, image analysis methods, which are less invasive low cost and more representative are now being developed. The main objective of this work was to estimate yield through data obtained in the frame of Vinbot project during the 2019 season. In this thesis, images of the grapevine variety Syrah taken in the laboratory and in the vineyards of the “Instituto Superior de Agronomia” in Lisbon were analyzed. In the laboratory the images were taken manually with an RGB camera, while in the field vines were imaged either manually and by the Vinbot robot. From these images, the number of visible berries were counted with MATLAB. From the laboratory values, the relationships between the number of visible berries and actual bunch weight and berry number were studied. From the data obtained in the field, it was analyzed the visibility of the berries at different levels of defoliation and the relationship between the area of visible bunches and the visible berries. Berry-by-berry occlusion showed a value of 6.4% at pea-size, 14.5% at veraison and 25% at maturation. In addition, high and significant determination coefficient were obtained between actual yield and visible berries. The comparison of estimated yield, obtained using the regression models with actual yield, showed an underestimation at all the three phonological stages. This low accuracy of the developed models show that the use of algorithms based on visible berry number on the images to estimate yield still needs further researchN/

Assessment of grape cluster yield components based on 3D descriptors using stereo vision

NOTICE: this is the author’s version of a work that was accepted for publication in Food Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Food Control, [Volume 50, April 2015, Pages 273–282] DOI 10.1016/j.foodcont.2014.09.004Wine quality depends mostly on the features of the grapes it is made from. Cluster and berry morphology are key factors in determining grape and wine quality. However, current practices for grapevine quality estimation require time-consuming destructive analysis or largely subjective judgment by experts. The purpose of this paper is to propose a three-dimensional computer vision approach to assessing grape yield components based on new 3D descriptors. To achieve this, firstly a partial three-dimensional model of the grapevine cluster is extracted using stereo vision. After that a number of grapevine quality components are predicted using SVM models based on new 3D descriptors. Experiments confirm that this approach is capable of predicting the main cluster yield components, which are related to quality, such as cluster compactness and berry size (R2 > 0.80, p < 0.05). In addition, other yield components: cluster volume, total berry weight and number of berries, were also estimated using SVM models, obtaining prediction R2 of 0.82, 0.83 and 0.71, respectively.This work has been partially funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA - Spanish National Institute for Agriculture and Food Research and Technology) through research project RTA2012-00062-C04-02, support of European FEDER funds, UPV-SP20120276 and AGL2011-23673 project.Ivorra Martínez, E.; Sánchez Salmerón, AJ.; Camarasa Baixauli, JG.; Diago, M.; Tardaguila, J. (2015). Assessment of grape cluster yield components based on 3D descriptors using stereo vision. Food Control. 50:273-282. https://doi.org/10.1016/j.foodcont.2014.09.004S2732825

PLANT DISESASE DETECTION BY USING IMAGE PROCESSING TECHNIQUES

In agriculture research of automatic leaf disease detection is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detects symptoms of the disease as soon as they appear on plant leaves. The term disease is usually used only for the destruction of live plants. This paper provides various methods used to study of leaf disease detection using image processing. The methods studies are for increasing throughput and reduction subjects arising from human experts in detecting the leaf disease [1].digital image processing is a technique used for enhancement of the image. To improve agricultural products automatic detection of symptoms is beneficial

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management

UNSUPERVISED LEARNING FOR RIPENESS ESTIMATION FROM GRAPE SEEDS IMAGES

Estimating the current stage of grape ripeness is a crucial step in wine making and becomes especially important during harvesting. Visual inspection of grape seeds is one method to achieve this goal without performing chemical analysis, however this method is prone to failure. In this paper, we propose an unsupervised visual inspection system for grape ripeness estimation using the Dirichlet Mixture Model (DMM). Experimental analysis using real world data demonstrates that our approach can be used to estimate different ripeness stages from unlabeled grape seeds catalogs

Characterization of vineyard's canopy through fuzzy clustering and svm over color images

In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process