Sensors in Agriculture

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

 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.