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Optoelectronic proximal sensing vehicle-mounted technologies in precision agriculture: A review

Author:
Pallottino, Federico, Antonucci, Francesca, Costa, Corrado, Bisaglia, Carlo, Figorilli, Simone, Menesatti, Paolo
Source:
Computers and electronics in agriculture 2019 v.162 pp. 859-873
ISSN:
0168-1699
Subject:
crop management, data collection, energy, fertilizers, greenhouse gases, human population, image analysis, management systems, normalized difference vegetation index, phenotype, precision agriculture, research and development, soil, temporal variation, thermography
Abstract:
Nowadays precision agriculture is undertaking a steep growth in terms of both, commercial products and research and development applications. This is rapidly changing the crop management system taking into grater consideration data acquisition and their elaboration, real-time or offline, to correctly consider the spatial and temporal variability of crop and soil factors. This step is due to reduce the energy inputs and the applications of chemicals and fertilizers, as well as Greenhouse gas (GHGs), while increasing the production. Sustainability is a must while trying to fulfil the food and fiber needs of the rapidly growing human population. In this light, the present review, more than reporting an exhaustive picture, aims attempt to show a clear panorama of such a context underlining the principal scientific researches and applications inherent optoelectronic proximal sensing vehicle-mounted technologies in precision agriculture. The review has been structured following the main sensors types and their core applications: grayscale/RGB imaging, Visible-Near infrared (Vis-NIR) and NIR, sensors dedicated to Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge index (NDRE), stereovision, thermography, combined sensors, sensors dedicated to phenotyping and others. Moreover, were considered two separate chapters regarding dedicated analytical approaches use and development and a term map analysis to graphically highlight the main clusters owing each to a specific subject area.
Agid:
6449248