Sensors in Agriculture, / Volume 2.
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 p...
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| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 electronic resource (354 p.) |
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| 100 | 1 | |a Moshou, Dimitrios |4 auth | |
| 245 | 1 | 0 | |a Sensors in Agriculture, |n Volume 2. |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (354 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 520 | |a 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. | ||
| 546 | |a English | ||
| 653 | |a optical sensor | ||
| 653 | |a spectral analysis | ||
| 653 | |a response surface sampling | ||
| 653 | |a sensor evaluation | ||
| 653 | |a electromagnetic induction | ||
| 653 | |a multivariate water quality parameters | ||
| 653 | |a mandarin orange | ||
| 653 | |a crop inspection platform | ||
| 653 | |a SPA-MLR | ||
| 653 | |a object tracking | ||
| 653 | |a feature selection | ||
| 653 | |a simultaneous measurement | ||
| 653 | |a diseases | ||
| 653 | |a genetic algorithms | ||
| 653 | |a processing of sensed data | ||
| 653 | |a electrochemical sensors | ||
| 653 | |a thermal image | ||
| 653 | |a ECa-directed soil sampling | ||
| 653 | |a handheld | ||
| 653 | |a recognition patterns | ||
| 653 | |a salt concentration | ||
| 653 | |a clover-grass | ||
| 653 | |a bovine embedded hardware | ||
| 653 | |a weed control | ||
| 653 | |a soil | ||
| 653 | |a field crops | ||
| 653 | |a vineyard | ||
| 653 | |a connected dominating set | ||
| 653 | |a water depth sensors | ||
| 653 | |a SS-OCT | ||
| 653 | |a wheat | ||
| 653 | |a striped stem-borer | ||
| 653 | |a silage | ||
| 653 | |a geostatistics | ||
| 653 | |a detection | ||
| 653 | |a NIR hyperspectral imaging | ||
| 653 | |a electronic nose | ||
| 653 | |a machine learning | ||
| 653 | |a virtual organizations of agents | ||
| 653 | |a packing density | ||
| 653 | |a data validation and calibration | ||
| 653 | |a dataset | ||
| 653 | |a Wi-SUN | ||
| 653 | |a temperature sensors | ||
| 653 | |a geoinformatics | ||
| 653 | |a gas sensor | ||
| 653 | |a X-ray fluorescence spectroscopy | ||
| 653 | |a vegetable oil | ||
| 653 | |a photograph-grid method | ||
| 653 | |a Vitis vinifera | ||
| 653 | |a WSN distribution algorithms | ||
| 653 | |a laser-induced breakdown spectroscopy | ||
| 653 | |a irrigation | ||
| 653 | |a quality assessment | ||
| 653 | |a energy efficiency | ||
| 653 | |a wireless sensor network (WSN) | ||
| 653 | |a geo-information | ||
| 653 | |a Fusarium | ||
| 653 | |a texture features | ||
| 653 | |a weeds | ||
| 653 | |a discrimination | ||
| 653 | |a big data | ||
| 653 | |a soil moisture sensors | ||
| 653 | |a meat spoilage | ||
| 653 | |a land cover | ||
| 653 | |a stereo imaging | ||
| 653 | |a near infrared sensors | ||
| 653 | |a biological sensing | ||
| 653 | |a compound sensor | ||
| 653 | |a pest management | ||
| 653 | |a moisture | ||
| 653 | |a plant localization | ||
| 653 | |a heavy metal contamination | ||
| 653 | |a artificial neural networks | ||
| 653 | |a spectral pre-processing | ||
| 653 | |a moisture content | ||
| 653 | |a apparent soil electrical conductivity | ||
| 653 | |a data fusion | ||
| 653 | |a semi-arid regions | ||
| 653 | |a smart irrigation | ||
| 653 | |a back propagation model | ||
| 653 | |a wireless sensor network | ||
| 653 | |a energy balance | ||
| 653 | |a light-beam | ||
| 653 | |a fluorescent measurement | ||
| 653 | |a agriculture | ||
| 653 | |a precision agriculture | ||
| 653 | |a deep learning | ||
| 653 | |a spectroscopy | ||
| 653 | |a hulled barely | ||
| 653 | |a dielectric probe | ||
| 653 | |a RPAS | ||
| 653 | |a water supply network | ||
| 653 | |a rice leaves | ||
| 653 | |a mobile app | ||
| 653 | |a gradient boosted machines | ||
| 653 | |a hyperspectral camera | ||
| 653 | |a one-class | ||
| 653 | |a nitrogen | ||
| 653 | |a LiDAR | ||
| 653 | |a total carbon | ||
| 653 | |a chemometrics analysis | ||
| 653 | |a rice | ||
| 653 | |a agricultural land | ||
| 653 | |a on-line vis-NIR measurement | ||
| 653 | |a CARS | ||
| 653 | |a obstacle detection | ||
| 653 | |a stratification | ||
| 653 | |a neural networks | ||
| 653 | |a regression estimator | ||
| 653 | |a Kinect | ||
| 653 | |a proximity sensing | ||
| 653 | |a distributed systems | ||
| 653 | |a pest | ||
| 653 | |a noninvasive detection | ||
| 653 | |a texture feature | ||
| 653 | |a soil mapping | ||
| 653 | |a classification | ||
| 653 | |a soil salinity | ||
| 653 | |a visible and near-infrared reflectance spectroscopy | ||
| 653 | |a germination | ||
| 653 | |a computer vision | ||
| 653 | |a hyperspectral imaging | ||
| 653 | |a diffusion | ||
| 653 | |a dielectric dispersion | ||
| 653 | |a UAS | ||
| 653 | |a random forests | ||
| 653 | |a case studies | ||
| 653 | |a total nitrogen | ||
| 653 | |a thermal imaging | ||
| 653 | |a cameras | ||
| 653 | |a dry matter composition | ||
| 653 | |a near-infrared | ||
| 653 | |a salt tolerance | ||
| 653 | |a deep convolutional neural networks | ||
| 653 | |a soil type classification | ||
| 653 | |a water management | ||
| 653 | |a preprocessing methods | ||
| 653 | |a wireless sensor networks (WSN) | ||
| 653 | |a remote sensing image classification | ||
| 653 | |a precision plant protection | ||
| 653 | |a radar | ||
| 653 | |a spatial variability | ||
| 653 | |a GF-1 satellite | ||
| 653 | |a plant disease | ||
| 653 | |a naked barley | ||
| 653 | |a leaf area index | ||
| 653 | |a CIE-Lab | ||
| 653 | |a change of support | ||
| 653 | |a radiative transfer model | ||
| 653 | |a 3D reconstruction | ||
| 653 | |a plant phenotyping | ||
| 653 | |a vine | ||
| 653 | |a near infrared | ||
| 653 | |a vegetation indices | ||
| 653 | |a remote sensing | ||
| 653 | |a greenhouse | ||
| 653 | |a time-series data | ||
| 653 | |a scattering | ||
| 653 | |a sensor | ||
| 653 | |a crop area | ||
| 653 | |a speckle | ||
| 653 | |a spatial data | ||
| 653 | |a grapevine breeding | ||
| 653 | |a wide field view | ||
| 653 | |a partial least squares-discriminant analysis | ||
| 653 | |a spiking | ||
| 653 | |a area frame sampling | ||
| 653 | |a chromium content | ||
| 653 | |a machine-learning | ||
| 653 | |a RGB-D sensor | ||
| 653 | |a pest scouting | ||
| 653 | |a PLS | ||
| 653 | |a Capsicum annuum | ||
| 653 | |a spatial-temporal model | ||
| 653 | |a drying temperature | ||
| 653 | |a boron tolerance | ||
| 653 | |a ambient intelligence | ||
| 653 | |a laser wavelength | ||
| 653 | |a fuzzy logic | ||
| 653 | |a dynamic weight | ||
| 653 | |a landslide | ||
| 653 | |a management zones | ||
| 653 | |a real-time processing | ||
| 653 | |a event detection | ||
| 653 | |a crop monitoring | ||
| 653 | |a apple shelf-life | ||
| 653 | |a rice field monitoring | ||
| 653 | |a wireless sensor | ||
| 653 | |a birth sensor | ||
| 653 | |a proximal sensor | ||
| 776 | |z 3-03897-744-6 | ||
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:55:12 Europe/Vienna |d 00 |f system |c marc21 |a 2019-11-10 04:18:40 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5337566030004498&Force_direct=true |Z 5337566030004498 |b Available |8 5337566030004498 | ||