Applications of Remote Image Capture System in Agriculture

• Autore: Molina Martínez José Miguel
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (310 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: 3D model; aerial photography; agricultural robot; agriculture; Agriculture 4.0; andosols; applications; artificial intelligence; automatic tomato harvesting; banana plantation; berries counting; budding rate; Cabernet Sauvignon; canopy temperature; classification performance; coffee leaf rust; convolutional neural network; decision making; deep learning; digital agriculture; digital image correlation; Dilated CNN; failure strain; fertirrigation; finite element method; Fourth Industrial Revolution; frequency-domain reflectometry; geometric consistency; grape yield estimate; greenhouse; Hill-Tsai failure criterion; ICP; image processing; land use; large-scale; low-cost thermography; machine learning; machine learning algorithms; machine learning regression; mixed pixels; moisture sensors; n/a; NDVI; non-transpiration baseline; non-water-stressed baselines; normalized difference vegetation index; Normalized Difference Vegetation Index; normalized difference water index; online operation; optimization algorithm; Panama TR4; Parrot Sequoia; pasture quality index; point clouds; precision agriculture; precision viticulture; Prunus avium L.; radiometric consistency; randomizable filtered classifier; reference evapotranspiration; reflectance maps; remote images; remote sensing; sandstone; segmentation; Sentinel-2; soil moisture; spatial vision; spectrometry; stem water potential; supplementation; SVM; systematic mapping study; teaching-learning; thermal indexes; total leaf area; U-Net; UAV; vegetation indices; water stress; winter wheat mapping
• Persona (resp. second.): García-MateosGinés; Molina MartínezJosé Miguel
• Sommario/riassunto: Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.
• Titolo autorizzato: Applications of Remote Image Capture System in Agriculture
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557895803321
• Lo trovi qui: Univ. Federico II