Reprint
Applications of Remote Image Capture System in Agriculture
Edited by
December 2020
310 pages
- ISBN978-3-03943-805-1 (Hardback)
- ISBN978-3-03943-806-8 (PDF)
This book is a reprint of the Special Issue Applications of Remote Image Capture System in Agriculture that was published in
Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
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.
Format
- Hardback
License
© 2021 by the authors; CC BY-NC-ND license
Keywords
SVM; budding rate; UAV; geometric consistency; radiometric consistency; point clouds; ICP; reflectance maps; vegetation indices; Parrot Sequoia; artificial intelligence; precision agriculture; agricultural robot; optimization algorithm; online operation; segmentation; coffee leaf rust; machine learning; deep learning; remote sensing; Fourth Industrial Revolution; Agriculture 4.0; failure strain; sandstone; digital image correlation; Hill–Tsai failure criterion; finite element method; reference evapotranspiration; moisture sensors; machine learning regression; frequency-domain reflectometry; randomizable filtered classifier; convolutional neural network; U-Net; segmentation; deep learning; land use; banana plantation; Panama TR4; aerial photography; remote images; systematic mapping study; agriculture; applications; total leaf area; mixed pixels; Cabernet Sauvignon; NDVI; Normalized Difference Vegetation Index; precision viticulture; 3D model; spatial vision; fertirrigation; teaching–learning; spectrometry; Sentinel-2; pasture quality index; normalized difference vegetation index; normalized difference water index; supplementation; decision making; digital agriculture; grape yield estimate; berries counting; deep learning; Dilated CNN; machine learning algorithms; classification performance; winter wheat mapping; Sentinel-2; large-scale; water stress; Prunus avium L.; stem water potential; low-cost thermography; thermal indexes; canopy temperature; non-water-stressed baselines; non-transpiration baseline; soil moisture; andosols; remote sensing; image processing; greenhouse; automatic tomato harvesting; precision agriculture; n/a