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Titre : Diagnostics of Plant Diseases Type de document : Monographie Auteurs : Dmitry Kurouski, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 ISBN/ISSN/EAN : 978-1-83962-516-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] blé (céréale)
[Termes IGN] image captée par drone
[Termes IGN] maladie phytosanitaire
[Termes IGN] Oryza (genre)
[Termes IGN] spectroscopie
[Termes IGN] surveillance agricoleIndex. décimale : 35.41 Applications de télédétection - végétation Résumé : (Editeur) Digital farming is an approach to farming in which crop yield is maximized while environmental impact is minimized. Integral to this approach is diagnostic sensing of plant disease and stress. This book examines innovative sensing technology such as satellite- and unmanned aerial vehicle (UAV)-based RGB and thermography imaging as well as hyperspectral, infrared, reflectance and Raman spectroscopy. Note de contenu :
1. Application of Spectroscopic Techniques in Early Detection of Fungal Plant Pathogens
By Ritesh Kumar, Shikha Pathak, Nishant Prakash, Upasna Priya and Abhijeet Ghatak
2. Diagnosis of Fungal Plant Pathogens Using Conventional and Molecular Approaches
By Monika C. Dayarathne, Amin U. Mridha and Yong Wang
3. UAV Remote Sensing: An Innovative Tool for Detection and Management of Rice Diseases
By Xin-Gen Zhou, Dongyan Zhang and Fenfang Lin
4. Blister Blight Disease of Tea: An Enigma
By Chayanika Chaliha and Eeshan Kalita
5. Spectroscopy Technology: An Innovative Tool for Diagnosis and Monitoring of Wheat Diseases
By Fenfang Lin, Dongyan Zhang, Xin-Gen Zhou and Yu Lei
6. The Trends in the Evaluation of Fusarium Wilt of Chickpea
By Chandan Singh and Deepak VyasNuméro de notice : 26721 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.88565 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.5772/intechopen.88565 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99502 Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)
Titre : Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations Type de document : Article/Communication Auteurs : Adnane Chakir , Auteur ; Pierre-Louis Frison , Auteur ; Saïd Khabba, Auteur ; Jamal Ezzahar, Auteur ; Ludovic Villard, Auteur ; Pascal Fanise, Auteur ; Nadia Ouaadi, Auteur ; V. Ledantec, Auteur ; Lionel Jarlan, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 3801 - 3804 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] cohérence temporelle
[Termes IGN] image Sentinel-SAR
[Termes IGN] Maroc
[Termes IGN] Olea europaea
[Termes IGN] vergerRésumé : (auteur) C-band radar remote sensing is a suitable tool for monitoring agricultural areas on a large scale, providing access to information on vegetation such as plant biomass, or on the surface water content of the soil. Recent studies suggest that the water state and the physiological functioning of trees influence radar response leading to marked daily profiles of both radar backscattering coefficient and temporal coherence. The objective of this paper is to make a preliminary comparison between the temporal evolution of Sentinel-1 radar data and in situ radar measurements over a Mediterranean olive orchard located in Morocco. In situ radar data consist in quad polarizations measurements realized from a 20m high tower, every 15 minutes, for the period extending from May 2019 to October 2020. Numéro de notice : C2021-051 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9553129 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9553129 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99415 Effects of different site preparation methods on the root development of planted Quercus petraea and Pinus nigra / Mathieu Dassot in New forests, vol 52 n° 1 (January 2021)
[article]
Titre : Effects of different site preparation methods on the root development of planted Quercus petraea and Pinus nigra Type de document : Article/Communication Auteurs : Mathieu Dassot , Auteur ; Catherine Collet, Auteur Année de publication : 2021 Article en page(s) : pp 17 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] phytobiologie
[Termes IGN] Pinus nigra
[Termes IGN] plantation forestière
[Termes IGN] Quercus sessiliflora
[Termes IGN] système radiculaire
[Vedettes matières IGN] BotaniqueRésumé : (auteur) Mechanical site preparation (MSP) is often performed prior to planting to improve the growth and survival of planted seedlings. In this study, we compared root development of 5-years-old Quercus petraea and Pinus nigra seedlings planted in plots that had been prepared with different methods, i.e. deep scarification, deep scarification combined with mounding-subsoiling, herbicide and a control without preparation. Seventy-two trees were excavated (36 per species) and their root system was measured by recording points in a three-dimensional space along their roots. The variation of the number of roots with depth and distance to root collar was assessed and analysed, as well as the root projection area. Our results showed that root development was better in the plots with mechanical preparation, for both Q. petraea and P. nigra, when compared to the control. Combining mounding to subsoiling made the roots extending deeper, especially for Q. petraea. A strong relationship was found between root projection area and root collar diameter, indicating the primary effect of lateral root spread on tree growth. The herbicide treatment induced the highest root growth, which raised questions about the potential negative effects of changes in soil properties caused by MSP methods. Numéro de notice : A2021-965 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11056-020-09781-7 En ligne : http://dx.doi.org/10.1007/s11056-020-09781-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101352
in New forests > vol 52 n° 1 (January 2021) . - pp 17 - 30[article]Ensemble learning methods on the space of covariance matrices : application to remote sensing scene and multivariate time series classification / Sara Akodad (2021)
Titre : Ensemble learning methods on the space of covariance matrices : application to remote sensing scene and multivariate time series classification Type de document : Thèse/HDR Auteurs : Sara Akodad, Auteur ; Christian Germain, Directeur de thèse ; Lionel Bombrun, Directeur de thèse Editeur : Bordeaux : Université de Bordeaux Année de publication : 2021 Importance : 220 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée pour obtenir le grade de Docteur de l'Université de Bordeaux, Spécialité Automatique, Productique, Signal et Image, Ingénierie cognitiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse multivariée
[Termes IGN] Castanea sativa
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] géométrie euclidienne
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] maladie phytosanitaire
[Termes IGN] matrice de covariance
[Termes IGN] processus gaussien
[Termes IGN] série temporelle
[Termes IGN] surveillance forestièreIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In view of the growing success of second-order statistics in classification problems, the work of this thesis has been oriented towards the development of learning methods in manifolds. Indeed, covariance matrices are symmetric positive definite matrices that live in a non-Euclidean space. It is therefore necessary to adapt the classical tools of Euclidean geometry to handle this type of data. To do that, we have proposed to exploit the log-Euclidean metric. This latter allows to project the set of covariance matrices on a tangent plane to the manifold defined at a reference point, classically chosen equal to the identity matrix, followed by a vectorization step to obtain the log-Euclidean representation. On this tangent plane, it is possible to define parametric Gaussian models as well as Gaussian mixture models. Nevertheless, this projection on a single tangent plane can induce distortions. In order to overcome this limitation, we have proposed a GMM model composed of several tangent planes, where the reference points are defined by the centers of each cluster.In view of the success of neural networks, in particular convolutional neural networks (CNNs), we have proposed two hybrid transfer learning approaches based on the covariance matrix computed locally and globally on the CNN convolutional layers’ outputs. The local approach relies on the covariance matrices extracted locally on the first layers of a CNN, which are then encoded by the Fisher vectors computed on their log-Euclidean representation, while for the global approach, a single covariance matrix is computed on the feature maps of the CNN deep layers. Moreover, in order to give more importance to the objects of interest present in the images, we proposed to use a covariance matrix weighted by the saliency information. Furthermore, in order to take advantage of both local and global aspects, these two approaches are subsequently combined in an ensemble strategy.On the other hand, the availability of multivariate time series has aroused the interest of the remote sensing community and more generally of machine learning researchers for the development of new learning strategies dedicated to supervised classification. In particular, methods based on the calculation of point-to-point distance between series. Moreover, two series belonging to the same class can evolve in different ways, which can induce temporal distortions (translation, compression, dilation, etc.). To avoid this, warping methods allow to align the time series. In order to extend this approach to time series of covariance matrices, while ensuring invariance to the re-parametrization of the series, we were interested in the TSRVF representation. In the same context, several ensemble methods have been proposed in the literature, including TCK, which relies on similarity computation to classify time series. We have proposed to extend this strategy to covariance matrices by introducing the SO-TCK approach which relies on the log-Euclidean representation of such matrices. Finally, the last axis of this thesis concerns the modeling of temporal trajectories of signals measured by the radar (Sentinel 1) and optical (Sentinel 2) sensors. In particular, we are interested in the forestry problem of the chestnut ink disease in the Montmorency forest. For this purpose, we developed classification and regression models to predict a health status score from the covariance matrix computed on multi-temporal radiometric attributes. Note de contenu : Introduction
1- Riemannian geometry and statistical modeling on the space of Symmetric Positive Definite (SPD) matrices
2- Ensemble learning approaches based on covariance pooling of CNN Features
3- Symmetric positive definite matrix time series classification
4- Forest health monitoring using Sentinel-1 and Sentinel-2 time series
Conclusions and perspectivesNuméro de notice : 28605 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automatique, Productique, Signal et Image, Ingénierie cognitique : Bordeaux : 2021 Organisme de stage : IMS DOI : sans En ligne : https://tel.hal.science/tel-03484011 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99446 Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping / Mthembeni Mngadi in Geocarto international, vol 36 n° 1 ([01/01/2021])
[article]
Titre : Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping Type de document : Article/Communication Auteurs : Mthembeni Mngadi, Auteur ; John Odindi, Auteur ; Kabir Peerbhay, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse discriminante
[Termes IGN] carte forestière
[Termes IGN] Eucalyptus (genre)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] KwaZulu-Natal (Afrique du Sud)
[Termes IGN] Pinus (genre)
[Termes IGN] télédétection spatialeRésumé : (Auteur) The successful launch and operation of the Sentinel satellite platform has provided access to freely available remotely sensed data useful for commercial forest species discrimination. Sentinel – 1 (S1) with a synthetic aperture radar (SAR) sensor and Sentinel – 2 (S2) multi-spectral sensor with additional and strategically positioned bands offer great potential for providing reliable information for discriminating and mapping commercial forest species. In this study, we sought to determine the value of S1 and S2 data characteristics in discriminating and mapping commercial forest species. Using linear discriminant analysis (LDA) algorithm, S2 multi-spectral imagery showed an overall classification accuracy of 84% (kappa = 0.81), with bands such as the red-edge (703.9–740.2 nm), narrow near infrared (835.1–864.8 nm), and short wave infrared (1613.7–2202.4 nm) particularly influential in discriminating individual forest species stands. When Sentinel 2’s spectral wavebands were fused with Sentinel 1’s (SAR) VV and VH polarimetric modes, overall classification accuracies improved to 87% (kappa = 0.83) and 88% (kappa = 0.85), respectively. These findings demonstrate the value of combining Sentinel’s multispectral and SAR structural information characteristics in improving commercial forest species discrimination. These, in addition to the sensors free availability, higher spatial resolution and larger swath width, offer unprecedented opportunities for improved local and large scale commercial forest species discrimination and mapping. Numéro de notice : A2021-050 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1585483 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1585483 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96719
in Geocarto international > vol 36 n° 1 [01/01/2021] . - pp 1 - 12[article]Réservation
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