Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status
Recently, a research paper was published on Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water in Agronomy, an international, scientific, peer-reviewed, open access journal published monthly online by MDPI.
The paper was written by Patricia López-García, Diego Intrigliolo, Miguel A. Moreno, Alejandro Martínez-Moreno, José Fernando Ortega, Eva Pilar Pérez-Álvarez, and Rocío Ballesteros. This research was funded by the Ministry of Science, Innovation and Universities, by the Government of Castilla-La Mancha, by FEDER funds, and by EU HORIZON.
CLICK HERE TO DOWNLOAD THE RESEARCH ARTICLE
Agronomy 2022, 12, 2122. https://doi.org/10.3390/agronomy12092122
In this study, drone surveying equipment from Microdrones was used for plant water status determination and, as a consequence, for irrigation management. The research used a Microdrones UAV to capture aerial images of a vineyard over 3 years through photogrammetric data captured using a combination of multispectral and conventional cameras.
With the help of the Microdrones integrated system, the research team determined that the use of machine learning methods such as ANN models are a powerful tool for processing remote sensing data obtained from UAVs to develop models for estimating water status.
They also found that the use of conventional RGB cameras is a promising alternative due to its lower cost compared with multispectral and thermal sensors, and the easier photogrammetric processing of the images. High-resolution RGB images permit the precise segmentation of vegetation image data, which is essential for avoiding soil effects and obtaining accurate GCC values.
If you are interested in learning more about Microdrones integrated systems, schedule a time to speak with one of our friendly representatives.