Grapevine stem water potential estimation based on sensor fusion

Noa Ohana-Levi, Igor Zachs, Nave Hagag, Liyam Shemesh, Yishai Netzer

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Estimating vine water status is essential for achieving the desired balance between wine grape quality and yield in viticulture management and irrigation planning. The growing demands of smart farming for extracting meaningful information from field data to support irrigation decisions may be facilitated using field monitoring technology along with advanced modeling algorithms. This research took place in a Vitis Vinifera cv. “Sauvignon Blanc” experimental plot within a commercial vineyard. The main objective was to generate a water stress estimation model for wine grapevines based on data fusion from multiple sensors. Data were collected using sensors from five monitored grapevines, each treated with a different water stress regime. The sensors provided measurements of trunk and fruit growth, leaf temperature, and soil water content (SWC) at depths of 20 and 40 cm. Additionally, meteorological station in the vineyard recorded the temperature, relative humidity, wind speed, and solar radiation. A daily-scale multivariate time series was composed using the data derived from the sensors. Stem water potential (SWP) values were measured for the monitored vines and analyzed against the multivariate time series of factors to define their interrelations and interactions. Finally, a prediction model for SWP estimation was defined using boosted regression trees (BRT) algorithm, with an optimized set of factors. Validation was conducted using comparative statistics between a randomly selected test set and a predicted set of SWP values achieved using the trained BRT model. After processing the data and eliminating most of the factors due to multicollinearity, the model was composed of daily maximum leaf temperature (LT), SWC at noon at 40 cm, tree water deficit (TWD), SWC daily amplitude at 20 cm, water input, vapor pressure deficit, and phenological stages. The highest contributor to the BRT model was maximum LT (44.5%), followed by SWC at noon at 40 cm (16.9%) and TWD (16%). The model performance was estimated using various measures and resulted in a correlation of 0.9 between the estimated SWP values and the test set. No significant differences were found between their means (t = -0.31, p-value = 0.76) and distributions (D = 0.13, p-value = 0.77). The RMSE was 0.16 MPa which corresponds to a 12% error when normalized to the range of the test set. The suggested sensor fusion approach for estimating SWP is a promising technique that integrates representative factors of the soil–water-plant-atmosphere continuum; it should be further examined in different climate regimes and vine varieties.

Original languageEnglish
Article number107016
JournalComputers and Electronics in Agriculture
StatePublished - Jul 2022


  • Deficit irrigation
  • Machine learning
  • Smart irrigation
  • Vitis vinifera
  • Water stress


Dive into the research topics of 'Grapevine stem water potential estimation based on sensor fusion'. Together they form a unique fingerprint.

Cite this