Sensors in Agriculture: Volume 1
MDPI, Jun 12, 2019 - Technology & Engineering - 346 pages
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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
accuracy Agric agricultural algorithm analysis application artificial neural networks average biofuel BPNN calibration set camera classification color Comput crop CrossRef data fusion detection developed discrimination diseases and pests error estimation European dataset evaluated Fusarium GF-1 WFV data Gold mustard oilseed Hessleskew hulled barley samples hyperspectral imaging Ida Gold mustard identify infected input irrigation laboratory leaf LiDAR lysimeter machine learning maize kernels marianum measurements method near-infrared neural networks optical optimal parameters performance pixels plant point cloud pre-processing prediction set PubMed random forests range regression remote sensing RMSE scintillometer selected sensor network sensor nodes shown in Figure simulations soil salinity soil samples soil types spatial speckle spectral spectroscopy support vector machine Table techniques temperature texture features tomato values variables vegetation viscosity wavelengths weed wine yield model