Sustainable Agriculture and Advances of Remote Sensing (Volume 2)
Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technol...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (322 p.) |
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| 245 | 1 | 0 | |a Sustainable Agriculture and Advances of Remote Sensing (Volume 2) |
| 246 | |a Sustainable Agriculture and Advances of Remote Sensing | ||
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| 300 | |a 1 electronic resource (322 p.) | ||
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| 520 | |a Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others. | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 650 | 7 | |a Geography |2 bicssc | |
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| 653 | |a pocket beaches | ||
| 653 | |a coastal management | ||
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| 653 | |a resource constraint | ||
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| 653 | |a evapotranspiration | ||
| 653 | |a penman-monteith equation | ||
| 653 | |a artificial neural network | ||
| 653 | |a canopy conductance | ||
| 653 | |a Ziz basin | ||
| 653 | |a water quality | ||
| 653 | |a satellite image analysis | ||
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| 653 | |a chlorophyll a | ||
| 653 | |a time series analysis | ||
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| 653 | |a modified normalized difference water index (MNDWI) | ||
| 653 | |a machine learning algorithm | ||
| 653 | |a hyperspectral | ||
| 653 | |a proximal sensing | ||
| 653 | |a panicle initiation | ||
| 653 | |a normalized difference vegetation index (NDVI) | ||
| 653 | |a green ring | ||
| 653 | |a internode-elongation | ||
| 653 | |a Sentinel 1 and 2 | ||
| 653 | |a Copernicus Sentinels | ||
| 653 | |a crop classification | ||
| 653 | |a food security | ||
| 653 | |a agricultural monitoring | ||
| 653 | |a data analysis | ||
| 653 | |a SAR | ||
| 653 | |a random forest | ||
| 653 | |a 3D bale wrapping method | ||
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| 653 | |a SVM | ||
| 653 | |a RF | ||
| 653 | |a Boufakrane River watershed | ||
| 653 | |a irrigation requirements | ||
| 653 | |a water resources | ||
| 653 | |a sustainable land use | ||
| 653 | |a agriculture | ||
| 653 | |a invasive plants | ||
| 653 | |a precision agriculture | ||
| 653 | |a rice farming | ||
| 653 | |a site-specific weed management | ||
| 653 | |a nitrogen prediction | ||
| 653 | |a 1D convolution neural networks | ||
| 653 | |a cucumber | ||
| 653 | |a crop yield improvement | ||
| 653 | |a mango leaf | ||
| 653 | |a CCA | ||
| 653 | |a vein pattern | ||
| 653 | |a leaf disease | ||
| 653 | |a cubic SVM | ||
| 653 | |a chlorophyll-a concentration | ||
| 653 | |a transfer learning | ||
| 653 | |a overfitting | ||
| 653 | |a data augmentation | ||
| 653 | |a guava disease | ||
| 653 | |a plant disease detection | ||
| 653 | |a crops diseases | ||
| 653 | |a entropy | ||
| 653 | |a features fusion | ||
| 653 | |a machine learning | ||
| 653 | |a object-based classification | ||
| 653 | |a density estimation | ||
| 653 | |a histogram | ||
| 653 | |a land use | ||
| 653 | |a crop fields | ||
| 653 | |a soil tillage | ||
| 653 | |a data fusion | ||
| 653 | |a multispectral | ||
| 653 | |a sensor | ||
| 653 | |a probe | ||
| 653 | |a temperature profile | ||
| 653 | |a forest roads | ||
| 653 | |a simulation | ||
| 653 | |a autonomous robots | ||
| 653 | |a smart agriculture | ||
| 653 | |a environmental protection | ||
| 653 | |a photogrammetry | ||
| 653 | |a path planning | ||
| 653 | |a internet of things | ||
| 653 | |a modeling | ||
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| 653 | |a modular robot | ||
| 653 | |a selective spraying | ||
| 653 | |a vision-based crop and weed detection | ||
| 653 | |a Faster R-CNN | ||
| 653 | |a YOLOv5 | ||
| 653 | |a band selection | ||
| 653 | |a CNN | ||
| 653 | |a NDVI | ||
| 653 | |a hyperspectral imaging | ||
| 653 | |a crops | ||
| 653 | |a urban flood | ||
| 653 | |a Sentinel-1a | ||
| 653 | |a Synthetic Aperture Radar (SAR) | ||
| 653 | |a 3D Convolutional Neural Network | ||
| 653 | |a multi-temporal data | ||
| 653 | |a land use classification | ||
| 653 | |a GIS | ||
| 653 | |a Coatzacoalcos | ||
| 653 | |a algorithms | ||
| 653 | |a clustering | ||
| 653 | |a pest control | ||
| 653 | |a site-specific | ||
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| 653 | |a rice plant | ||
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| 653 | |a sustainable agriculture | ||
| 653 | |a green technologies | ||
| 653 | |a Internet of Things | ||
| 653 | |a natural resources | ||
| 653 | |a sustainable environment | ||
| 653 | |a IoT ecosystem | ||
| 653 | |a hyperspectral remoting sensing | ||
| 653 | |a crop mapping | ||
| 653 | |a image classification | ||
| 653 | |a deep transfer learning | ||
| 653 | |a hyperparameter optimization | ||
| 653 | |a metaheuristic | ||
| 653 | |a soil attribute | ||
| 653 | |a ordinary Kriging | ||
| 653 | |a rational sampling numbers | ||
| 653 | |a spatial heterogeneity | ||
| 653 | |a sampling | ||
| 653 | |a soil pH | ||
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| 653 | |a Land Use/Land Cover | ||
| 653 | |a LISS-III | ||
| 653 | |a Landsat | ||
| 653 | |a Vision Transformer | ||
| 653 | |a Bidirectional long-short term memory | ||
| 653 | |a Google Earth Engine | ||
| 653 | |a Explainable Artificial Intelligence | ||
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| ADM | |b 2023-12-15 06:00:20 Europe/Vienna |f system |c marc21 |a 2022-11-05 21:33:14 Europe/Vienna |g false | ||
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