The mechanical harvesting of tart cherries has been a standard practice since the 1960’s but no technology has been proposed to map orchard yields. This study addresses this gap by introducing a low-cost yield monitor that tracks the distance between fruit bin changes using an ultrasonic proximity sensor, Global Navigation Satellite System (GNSS) positioning, and a microcomputer to collect and store the data. Results demonstrated agreement between the used GNSS receiver and differential-GNSS receivers, with positional errors under 4 m (m). The average final weight of 70 bins measured at the orchards was 512 kg, while the average final weight for 2,142 bins measured at the processing plant was 483 kg. The maximum difference in average final bin weight between seven operators was up to 24 kg in the orchards. Two orchard blocks were mapped over 2023 and 2024 with yields ranging from 6 to 46 Mg ha ^− 1 . The time taken by operators to harvest a single tree was between 26 and 37 s. Beyond its immediate practical advantages, our method also paves the way for future innovations in precision agriculture. Its flexible design allows it to be adapted for use with other crops that employ shake-and-catch harvesting systems. The proposed method offers an affordable solution to provide tart cherry growers with valuable insights about yield variability and harvest labor efficiency.

Accurate estimates of actual evapotranspiration (ET _a ) are essential for irrigation management in vineyards. This study locally calibrated and validated the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model for California vineyard systems using Landsat 8 imagery and multi-year eddy covariance (EC) measurements collected between 2013 and 2016 in the Central Valley of California. Two calibration approaches were implemented: k-fold cross-validation and a split-sample (two-year) strategy. SAFER performance was evaluated using both closed and unclosed energy balance EC fluxes to assess the influence of energy balance closure on model calibration and validation. Energy balance closure ratios ranged from 0.67 to 0.95, with moderate interannual variability and lower closure observed during some periods. Local calibration of SAFER resulted in regression coefficients that differed substantially from the original parametrization. Compared to the original coefficients, root mean square error (RMSE) was reduced by approximately 71–77%, with values below 0.70 mm d ^− 1 for both calibration approaches. Model validation showed strong agreement between SAFER ET _a and EC observations after calibration. RMSE ranged from 0.57 to 0.71 mm d ^− 1 and mean absolute error (MAE) ranged from 0.46 to 0.55 mm d ^− 1 . Overall, the results indicate that SAFER provides reliable estimates of daily vineyard ET _a when locally calibrated, supporting its application for field-scale irrigation management in perennial cropping systems.

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). Six equations were generated for different data-availability scenarios and validated using a leave-one-tree-out (modified k-fold) approach, resulting in Ψ_stem estimates with R² values ranging from 0.67 to 0.80 and root mean square errors (RMSE) ranging from 0.11 to 0.08 MPa. Notably, SR was able to produce interpretable equations that enhance model transparency and transferability. Model robustness was further confirmed using an independent dataset from a different location. To our knowledge, this is the first application of SR for Ψ_stem estimation, offering a scalable and interpretable tool to support irrigation management in tart cherry orchards.