Sensors in Agriculture

• Váldodahkki: Moshou, Dimitrios (auth)
• Materiálatiipa: Elektrovnnalaš Girjji oassi
• Giella: eaŋgalasgiella
• Almmustuhtton: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Fáttát: History of engineering & technology; optical sensor; spectral analysis; response surface sampling; sensor evaluation; electromagnetic induction; multivariate water quality parameters; mandarin orange; crop inspection platform; SPA-MLR; object tracking; feature selection; simultaneous measurement; diseases; genetic algorithms; processing of sensed data; electrochemical sensors; thermal image; ECa-directed soil sampling; handheld; recognition patterns; salt concentration; clover-grass; bovine embedded hardware; weed control; soil; field crops; vineyard; connected dominating set; water depth sensors; SS-OCT; wheat; striped stem-borer; silage; geostatistics; detection; NIR hyperspectral imaging; electronic nose; machine learning; virtual organizations of agents; packing density; data validation and calibration; dataset; Wi-SUN; temperature sensors; geoinformatics; gas sensor; X-ray fluorescence spectroscopy; vegetable oil; photograph-grid method; Vitis vinifera; WSN distribution algorithms; laser-induced breakdown spectroscopy; irrigation; quality assessment; energy efficiency; wireless sensor network (WSN); geo-information; Fusarium; texture features; weeds; discrimination; big data; soil moisture sensors; meat spoilage; land cover; stereo imaging; near infrared sensors; biological sensing; compound sensor; pest management; moisture; plant localization; heavy metal contamination; artificial neural networks; spectral pre-processing; moisture content; apparent soil electrical conductivity; data fusion; semi-arid regions; smart irrigation; back propagation model; wireless sensor network; energy balance; light-beam; fluorescent measurement; agriculture; precision agriculture; deep learning; spectroscopy; hulled barely; dielectric probe; RPAS; water supply network; rice leaves; mobile app; gradient boosted machines; hyperspectral camera; one-class; nitrogen; LiDAR; total carbon; chemometrics analysis; rice; agricultural land; on-line vis-NIR measurement; CARS; obstacle detection; stratification; neural networks; regression estimator; Kinect; proximity sensing; distributed systems; pest; noninvasive detection; texture feature; soil mapping; classification; soil salinity; visible and near-infrared reflectance spectroscopy; germination; computer vision; hyperspectral imaging; diffusion; dielectric dispersion; UAS; random forests; case studies; total nitrogen; thermal imaging; cameras; dry matter composition; near-infrared; salt tolerance; deep convolutional neural networks; soil type classification; water management; preprocessing methods; wireless sensor networks (WSN); remote sensing image classification; precision plant protection; radar; spatial variability; GF-1 satellite; plant disease; naked barley; leaf area index; CIE-Lab; change of support; radiative transfer model; 3D reconstruction; plant phenotyping; vine; near infrared; vegetation indices; remote sensing; greenhouse; time-series data; scattering; sensor; crop area; speckle; spatial data; grapevine breeding; wide field view; partial least squares-discriminant analysis; spiking; area frame sampling; chromium content; machine-learning; RGB-D sensor; pest scouting; PLS; Capsicum annuum; spatial-temporal model; drying temperature; boron tolerance; ambient intelligence; laser wavelength; fuzzy logic; dynamic weight; landslide; management zones; real-time processing; event detection; crop monitoring; apple shelf-life; rice field monitoring; wireless sensor; birth sensor; proximal sensor
• Liŋkkat: DOAB: download the publication; DOAB: description of the publication