TY  - GEN
AU  - Moshou,Dimitrios
TI  - Sensors in Agriculture
SN  - books978-3-03897-413-0
PY  - 2019///
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - optical sensor
KW  - spectral analysis
KW  - response surface sampling
KW  - sensor evaluation
KW  - electromagnetic induction
KW  - multivariate water quality parameters
KW  - mandarin orange
KW  - crop inspection platform
KW  - SPA-MLR
KW  - object tracking
KW  - feature selection
KW  - simultaneous measurement
KW  - diseases
KW  - genetic algorithms
KW  - processing of sensed data
KW  - electrochemical sensors
KW  - thermal image
KW  - ECa-directed soil sampling
KW  - handheld
KW  - recognition patterns
KW  - salt concentration
KW  - clover-grass
KW  - bovine embedded hardware
KW  - weed control
KW  - soil
KW  - field crops
KW  - vineyard
KW  - connected dominating set
KW  - water depth sensors
KW  - SS-OCT
KW  - wheat
KW  - striped stem-borer
KW  - silage
KW  - geostatistics
KW  - detection
KW  - NIR hyperspectral imaging
KW  - electronic nose
KW  - machine learning
KW  - virtual organizations of agents
KW  - packing density
KW  - data validation and calibration
KW  - dataset
KW  - Wi-SUN
KW  - temperature sensors
KW  - geoinformatics
KW  - gas sensor
KW  - X-ray fluorescence spectroscopy
KW  - vegetable oil
KW  - photograph-grid method
KW  - Vitis vinifera
KW  - WSN distribution algorithms
KW  - laser-induced breakdown spectroscopy
KW  - irrigation
KW  - quality assessment
KW  - energy efficiency
KW  - wireless sensor network (WSN)
KW  - geo-information
KW  - Fusarium
KW  - texture features
KW  - weeds
KW  - discrimination
KW  - big data
KW  - soil moisture sensors
KW  - meat spoilage
KW  - land cover
KW  - stereo imaging
KW  - near infrared sensors
KW  - biological sensing
KW  - compound sensor
KW  - pest management
KW  - moisture
KW  - plant localization
KW  - heavy metal contamination
KW  - artificial neural networks
KW  - spectral pre-processing
KW  - moisture content
KW  - apparent soil electrical conductivity
KW  - data fusion
KW  - semi-arid regions
KW  - smart irrigation
KW  - back propagation model
KW  - wireless sensor network
KW  - energy balance
KW  - light-beam
KW  - fluorescent measurement
KW  - agriculture
KW  - precision agriculture
KW  - deep learning
KW  - spectroscopy
KW  - hulled barely
KW  - dielectric probe
KW  - RPAS
KW  - water supply network
KW  - rice leaves
KW  - mobile app
KW  - gradient boosted machines
KW  - hyperspectral camera
KW  - one-class
KW  - nitrogen
KW  - LiDAR
KW  - total carbon
KW  - chemometrics analysis
KW  - rice
KW  - agricultural land
KW  - on-line vis-NIR measurement
KW  - CARS
KW  - obstacle detection
KW  - stratification
KW  - neural networks
KW  - regression estimator
KW  - Kinect
KW  - proximity sensing
KW  - distributed systems
KW  - pest
KW  - noninvasive detection
KW  - texture feature
KW  - soil mapping
KW  - classification
KW  - soil salinity
KW  - visible and near-infrared reflectance spectroscopy
KW  - germination
KW  - computer vision
KW  - hyperspectral imaging
KW  - diffusion
KW  - dielectric dispersion
KW  - UAS
KW  - random forests
KW  - case studies
KW  - total nitrogen
KW  - thermal imaging
KW  - cameras
KW  - dry matter composition
KW  - near-infrared
KW  - salt tolerance
KW  - deep convolutional neural networks
KW  - soil type classification
KW  - water management
KW  - preprocessing methods
KW  - wireless sensor networks (WSN)
KW  - remote sensing image classification
KW  - precision plant protection
KW  - radar
KW  - spatial variability
KW  - GF-1 satellite
KW  - plant disease
KW  - naked barley
KW  - leaf area index
KW  - CIE-Lab
KW  - change of support
KW  - radiative transfer model
KW  - 3D reconstruction
KW  - plant phenotyping
KW  - vine
KW  - near infrared
KW  - vegetation indices
KW  - remote sensing
KW  - greenhouse
KW  - time-series data
KW  - scattering
KW  - sensor
KW  - crop area
KW  - speckle
KW  - spatial data
KW  - grapevine breeding
KW  - wide field view
KW  - partial least squares-discriminant analysis
KW  - spiking
KW  - area frame sampling
KW  - chromium content
KW  - machine-learning
KW  - RGB-D sensor
KW  - pest scouting
KW  - PLS
KW  - Capsicum annuum
KW  - spatial-temporal model
KW  - drying temperature
KW  - boron tolerance
KW  - ambient intelligence
KW  - laser wavelength
KW  - fuzzy logic
KW  - dynamic weight
KW  - landslide
KW  - management zones
KW  - real-time processing
KW  - event detection
KW  - crop monitoring
KW  - apple shelf-life
KW  - rice field monitoring
KW  - wireless sensor
KW  - birth sensor
KW  - proximal sensor
N1  - Open Access
N2  - Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed
UR  - https://mdpi.com/books/pdfview/book/1343
UR  - https://directory.doabooks.org/handle/20.500.12854/59230
ER  -