Robotics-based vineyard water potential monitoring at high resolution - Saiz-Rubio, Verónica; Rovira-Más, Francisco; Cuenca-Cuenca, Andrés; Alves, Fernando (Computers and electronics in agriculture, 2021)
[Display omitted] •An Autonomous Ground Vehicle is used as an on-the-go non-destructive data collector.•Maps of estimated water potential were built from five crop and ambient variables.•Water potential multivariate model yielded a coefficient of determination of 0.69.•Temperature difference and Photochemical Reflectance Index correlated to R2 = 0.75.•Leaf water potential and Temperature difference correlated to R2 = 0.61. The purpose of this research is deploying a proximal sensing solution using non-invasive and cost-effective sensors onboard an Autonomous Ground Vehicle (AGV) as a feasible way for building high-resolution maps of water potential in vineyards. The final objective is offering growers a practical system to make decisions about water management, especially for arid climatic conditions. The monitoring AGV was entirely developed within this research context, and as a result, it is a machine specifically designed to endure off-road conditions and harsh environments. The autonomous vehicle served as a massive, non-invasive, and on-the-go data collector robotic platform. The sensors used for measuring the relevant field variables were two spectral reflectance sensors (SRS), an infrared radiometer, and an on-board weather sensor. The collected data were displayed on comprehensible grid maps using the Local Tangent Plane (LTP) coordinate system. The proposed model has a coefficient of determination R2 of 0.69, and results from combining six parameters: the canopy and air temperatures (as the temperature difference), the relative humidity, the altitude difference, the Normalized Difference Vegetation Index (NDVI), and the Photochemical Reflectance Index (PRI). The strongest relationships found in this study were between the temperature difference and PRI, with an R2 of 0.75, and the temperature difference with the leaf water potential with an R2 of 0.61. The practical use of these high-resolution maps includes irrigation scheduling and harvest zoning for sorting grape quality, with a further use as inputs to complex artificial intelligence algorithms considering larger areas or complementing airborne data. Future improvements to make the models more robust and versatile will entail considering additional variables, locations, or grapevine cultivars, and even other crops grown in vertical trellis systems.