Reprint

Sensors in Agriculture

Volume 1

Edited by
June 2019
346 pages
  • ISBN978-3-03897-412-3 (Paperback)
  • ISBN978-3-03897-413-0 (PDF)

This book is a reprint of the Special Issue Sensors in Agriculture that was published in

This book is part of the book set Sensors in Agriculture

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

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.

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