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This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input.
Chantal Hajjar; Ghassan Ghattas; Maya Sarkis; Yolla Chamoun. Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images. Remote Sensing 2021, 13, 2992 .
AMA StyleChantal Hajjar, Ghassan Ghattas, Maya Sarkis, Yolla Chamoun. Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images. Remote Sensing. 2021; 13 (15):2992.
Chicago/Turabian StyleChantal Hajjar; Ghassan Ghattas; Maya Sarkis; Yolla Chamoun. 2021. "Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images." Remote Sensing 13, no. 15: 2992.
In this paper, we propose to estimate the moisture of vineyard soils from digital photography using machine learning methods. Two nonlinear regression models are implemented: a multilayer perceptron (MLP) and a support vector regression (SVR). Pixels coded with RGB colour model extracted from soil digital images along with the associated known soil moisture levels are used to train both models in order to predict moisture content from newly acquired images. The study is conducted on samples of six soil types collected from Chateau Kefraya terroirs in Lebanon. Both methods succeeded in forecasting moisture giving high correlation values between the measured moisture and the predicted moisture when tested on unknown data. However, the method based on SVR outperformed the one based on MLP yielding Pearson correlation coefficient values ranging from 0.89 to 0.99. Moreover, it is a simple and noninvasive method that can be adopted easily to detect vineyards soil moisture.
Chantal Saad Hajjar; Celine Hajjar; Michel Esta; Yolla Ghorra Chamoun. Machine learning methods for soil moisture prediction in vineyards using digital images. E3S Web of Conferences 2020, 167, 02004 .
AMA StyleChantal Saad Hajjar, Celine Hajjar, Michel Esta, Yolla Ghorra Chamoun. Machine learning methods for soil moisture prediction in vineyards using digital images. E3S Web of Conferences. 2020; 167 ():02004.
Chicago/Turabian StyleChantal Saad Hajjar; Celine Hajjar; Michel Esta; Yolla Ghorra Chamoun. 2020. "Machine learning methods for soil moisture prediction in vineyards using digital images." E3S Web of Conferences 167, no. : 02004.
The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map for interval-valued data based on adaptive Mahalanobis distances in order to do clustering of interval data with topology preservation. Two methods based on the batch training algorithm for the self-organizing maps are proposed. The first method uses a common Mahalanobis distance for all clusters. In the second method, the algorithm starts with a common Mahalanobis distance per cluster and then switches to use a different distance per cluster. This process allows a more adapted clustering for the given data set. The performances of the proposed methods are compared and discussed using artificial and real interval data sets.
Chantal Hajjar; Hani Hamdan. Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. Neural Networks 2013, 46, 124 -132.
AMA StyleChantal Hajjar, Hani Hamdan. Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. Neural Networks. 2013; 46 ():124-132.
Chicago/Turabian StyleChantal Hajjar; Hani Hamdan. 2013. "Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances." Neural Networks 46, no. : 124-132.
The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map for interval-valued data based on adaptive Mahalanobis distances in order to do clustering of interval data with topology preservation. Two methods based on the batch training algorithm for the self-organizing maps are proposed. The first method uses a common Mahalanobis distance for all clusters. In the second method, the algorithm starts with a common Mahalanobis distance per cluster and then switches to use a different distance per cluster. This process allows a more adapted clustering for the given data set. The performances of the proposed methods are compared and discussed.
Chantal Hajjar; Hani Hamdan. Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. The 2013 International Joint Conference on Neural Networks (IJCNN) 2013, 1 -6.
AMA StyleChantal Hajjar, Hani Hamdan. Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. The 2013 International Joint Conference on Neural Networks (IJCNN). 2013; ():1-6.
Chicago/Turabian StyleChantal Hajjar; Hani Hamdan. 2013. "Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances." The 2013 International Joint Conference on Neural Networks (IJCNN) , no. : 1-6.
The Self-Organizing Maps have been widely used as multidimensional unsupervised classifiers. The aim of this paper is to develop a self-organizing map for interval data. Due to the increasing use of such data in Data Mining, many clustering methods for interval data have been proposed this last decade. In this paper, we propose an algorithm to train the self-organizing map for interval data. We use the city-block distance to compare two vectors of intervals. In order to show the usefulness of our approach, we apply the self-organizing map on real interval data issued from meteorological stations in France.
Chantal Hajjar; Hani Hamdan; Omar Hammami; Daniel Krob; Jean-Luc Voirin. Self-Organizing Map Based on City-Block Distance for Interval-Valued Data. Complex Systems Design & Management 2012, 281 -292.
AMA StyleChantal Hajjar, Hani Hamdan, Omar Hammami, Daniel Krob, Jean-Luc Voirin. Self-Organizing Map Based on City-Block Distance for Interval-Valued Data. Complex Systems Design & Management. 2012; ():281-292.
Chicago/Turabian StyleChantal Hajjar; Hani Hamdan; Omar Hammami; Daniel Krob; Jean-Luc Voirin. 2012. "Self-Organizing Map Based on City-Block Distance for Interval-Valued Data." Complex Systems Design & Management , no. : 281-292.
The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This chapter presents a self-organizing map to do unsupervised clustering for interval data. This map uses an extension of the Euclidian distance to compute the proximity between two vectors of intervals where each neuron represents a cluster. The performance of this approach is then illustrated and discussed while applied to temperature interval data coming from Chinese meteorological stations. The bounds of each interval are the measured minimal and maximal values of the temperature. In the presented experiments, stations of similar climate regions are assigned to the same neuron or to a neighbor neuron on the map.
Chantal Hajjar; Hani Hamdan. Clustering of Interval Data Using Self-Organizing Maps – Application to Meteorological Data. Topics in Intelligent Engineering and Informatics 2012, 1, 135 -146.
AMA StyleChantal Hajjar, Hani Hamdan. Clustering of Interval Data Using Self-Organizing Maps – Application to Meteorological Data. Topics in Intelligent Engineering and Informatics. 2012; 1 ():135-146.
Chicago/Turabian StyleChantal Hajjar; Hani Hamdan. 2012. "Clustering of Interval Data Using Self-Organizing Maps – Application to Meteorological Data." Topics in Intelligent Engineering and Informatics 1, no. : 135-146.
The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map to do unsupervised clustering for mixed feature-type symbolic data while preserving the topology of the data. A preprocessing technique prior to clustering is needed in order to homogenize the data. Every mixed feature-type vector is transformed into a vector of histograms. The resulting data set is used to train the self-organizing map using the batch algorithm. Similar input vectors will be allocated to the same neuron or to a neighbor neuron on the map. The performance of this approach is then illustrated and discussed while applied to real interval and mixed feature-type symbolic data sets.
Chantal Hajjar; Hani Hamdan. Self-organizing maps for mixed feature-type symbolic data. 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2012, 000135 -000140.
AMA StyleChantal Hajjar, Hani Hamdan. Self-organizing maps for mixed feature-type symbolic data. 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 2012; ():000135-000140.
Chicago/Turabian StyleChantal Hajjar; Hani Hamdan. 2012. "Self-organizing maps for mixed feature-type symbolic data." 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) , no. : 000135-000140.
The Self-Organizing Maps have been widely used as multidimensional unsupervised classifiers. The aim of this paper is to develop a self-organizing map for interval data. Due to the increasing use of such data in Data Mining, many clustering methods for interval data have been proposed this last decade. In this paper, we propose an algorithm to train the self-organizing map for interval data. We use the Hausdorff distance to compare two vectors of intervals. In order to show the usefulness of our approach, we apply the self-organizing map on real interval data issued from meteorological stations in China.
Chantal Hajjar; Hani Hamdan. Self-organizing map based on hausdorff distance for interval-valued data. 2011 IEEE International Conference on Systems, Man, and Cybernetics 2011, 1747 -1752.
AMA StyleChantal Hajjar, Hani Hamdan. Self-organizing map based on hausdorff distance for interval-valued data. 2011 IEEE International Conference on Systems, Man, and Cybernetics. 2011; ():1747-1752.
Chicago/Turabian StyleChantal Hajjar; Hani Hamdan. 2011. "Self-organizing map based on hausdorff distance for interval-valued data." 2011 IEEE International Conference on Systems, Man, and Cybernetics , no. : 1747-1752.