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.
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.
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