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Auteur Gonzalo Pajares Martinsanz |
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Titre : Image processing in agriculture and forestry Type de document : Monographie Auteurs : Gonzalo Pajares Martinsanz, Éditeur scientifique ; Francisco Rovira-Más, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2018 Importance : 222 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 9783038970972 9783038970989 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] changement d'occupation du sol
[Termes IGN] chlorophylle
[Termes IGN] couvert forestier
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] indice foliaire
[Termes IGN] instrument embarqué
[Termes IGN] phénologie
[Termes IGN] positionnement en intérieur
[Termes IGN] reconstruction 3D
[Termes IGN] teneur en eau de la végétation
[Termes IGN] traitement automatique de données
[Termes IGN] vision par ordinateurRésumé : (édition) Image processing in agriculture and forestry represents a challenge towards the automation of tasks for better performances. Agronomists, computer and robotics engineers, and agricultural machinery industry manufacturers now have at their disposal a book containing a collection of methods, procedures, designs, and descriptions at the technological forefront, which serves as an important support and aid for the implementation and development of their own ideas.The book describes: (1) Applications (canopy on trees, aboveground biomass, phenotyping, chlorophyll, leaf area index, water and nutrient content, land cover change, soil properties, and secure autonomous navigation); (2) Imaging devices onboard robots, unmanned aerial vehicles (UAVs), and satellites operating at different spectral ranges (visible, infrared, hyper-multispectral bands, and radar), as well as guidelines for selecting machine vision systems in outdoor environments; and (3) (Specific computer vision methods (generic and convolutional neural networks, machine learning, specific segmentation approaches, vegetation indices, and three-dimensional (3D) reconstruction). Note de contenu : Preface
1- Machine-vision systems selection for agricultural vehicles
2- Precise navigation of small agricultural robots in sensitive areas with a smart plant camera
3- Using deep learning to challenge safety standard for highly autonomous machines in agriculture
4- 3D reconstruction of plant/tree canopy using monocular and binocular vision
5- Peach flower monitoring using aerial multispectral imaging
6- Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks
7- Non-parametric retrieval of aboveground biomass in Siberian boreal forests with ALOS PALSAR interferometric coherence and backscatter intensity
8- Imaging for high-throughput phenotyping in energy sorghum
9- Viewing geometry sensitivity of commonly used vegetation indices towards the estimation of biophysical variables in orchards
10- Estimating mangrove biophysical variables using WorldView-2 satellite data: Rapid creek, Northern Territory, Australia
11- Land cover change image analysis for Assateague Island National Seashore following hurricane Sandy
12- Automated soil physical parameter assessment using smartphone and digital camera imageryNuméro de notice : 25921 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Monographie En ligne : https://doi.org/10.3390/books978-3-03897-098-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96137