Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)
Tomas Poblete, Samuel Ortega-Farias, Miguel Angel Moreno, Matthew Bardeen
SENSORS | MDPI | Published : 2017
Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models..View full abstract
Awarded by Chilean government through the project CONICYT-PFCHA
Awarded by Chilean government through the project FONDECYT
Awarded by Spanish Ministry of Education and Science (MEC)
This study was supported by the Chilean government through the projects CONICYT-PFCHA (No. 2014-21140229) and FONDECYT (No. 1160997) and by the Universidad de Talca through the research program Adaptation of Agriculture to Climate Change (A2C2). The authors wish to express their gratitude to the Spanish Ministry of Education and Science (MEC), for funding the projects AGL2011-30328-C02-01 and AGL2014-59747-C2-1-R (Co-funded by FEDER).