Optimal Remotely Piloted Aircraft System Imaging and Processing for Horticultural Applications
Remotely Piloted Aircraft Systems (RPAS) have become popular platforms to monitor crop’s growing condition for the past years. When adapting to horticultural crop monitoring, the measurement of the lower part of canopy, or sub-canopy, directly affect the understanding of canopy structure, tree structure, and crop condition, which is important to productivity analysis compared to the crops without tree crown. However, to acquire the most effective information for horticultural applications, the understanding of an optimal RPAS configuration still needs further experiments to be fulfilled to collect, correct, and extract the appropriate data. Optimal RPAS configuration involves specification of all of the data collection and processing operations which control the type and accuracy of data collected and produced from RPAS surveys. This includes: (1) for data collection, the parameters that affect image spatial, spectral, radiometric and temporal resolution and positional accuracy and precision; (2) for data correction, the geometric and radiometric correction algorithms; and (3) for data processing, the algorithms used to deliver spatial data products and information. Therefore, this research aims to develop, test, and deliver an optimal RPAS configuration to enable the use of RPAS in horticultural applications for mapping canopy dimensions (tree height, crown dimensions, distribution of foliage).