Novel algorithms for high-resolution prediction of canopy evapotranspiration in grapevine

Developing low-cost technology for custom water delivery to individual or small groups of plants is a critical next step to advance precision irrigation. Current systems for estimating evapotranspiration (ET), or plant water use, work on the scale of a full vineyard (e.g., 3–5 acres) or the scale of a single vine, but at a cost that prohibits monitoring past a small number of representative vines. To develop and evaluate low-cost ET sensors for individual grapevines, we used three head-pruned Zinfandel vines in pots and placed them on load cells to collect continuous weights indicative of actual ET. We mounted research-grade sensors for humidity, temperature, and wind speed on each vine and saved data at 2-minute intervals during three growing seasons. We developed three models based on first principles (Convective Mass Transfer or Mass Balance approaches) or simple correlations to predict actual single-plant ET from these data. We present here the results of a multi-year trial at the UC-Davis RMI vineyard to illustrate the performance of each of the models for ET estimation. Relative model performance was assessed by comparing model predictions to ground truth data provided by measurements from load cells–including assessments of estimated instantaneous ET rate, estimated cumulative water use over a one-hour window surrounding solar noon, and estimated cumulative water use over a full 24-hour period. The three algorithms developed consistently performed well, with single vine ET rate predictions showing a strong linear relationship with ground truth (range in r2 over three seasons CMT r2 = 0.61–0.86; MB r2 = 0.07–0.91; EM r2 = 0.57–0.92). The MB approach, which includes two measurements of relative humidity and temperature, was the most variable, likely due to the impact of sensor placement. In all seasons, we also examined the trend in the plant scaling factor found in each model, deemed As, which, based on model theory, is a function of vine size. Taken together, these results suggest that high-resolution irrigation (HRI) models are a promising new method for ET estimation at the single plant level

Bioinspired Interlocked and Hierarchical Design of ZnO Nanowire Arrays for Static and Dynamic Pressure-Sensitive Electronic Skins

The development of electronic skin (e-skin) is of great importance in human-like robotics, healthcare, wearable electronics, and medical applications. In this paper, a bioinspired e-skin design of hierarchical micro-and nanostructured ZnO nanowire (NW) arrays in an interlocked geometry is suggested for the sensitive detection of both static and dynamic tactile stimuli through piezoresistive and piezoelectric transduction modes, respectively. The interlocked hierarchical structures enable a stress-sensitive variation in the contact area between the interlocked ZnO NWs and also the efficient bending of ZnO NWs, which allow the sensitive detection of both static and dynamic tactile stimuli. The flexible e-skin in a piezoresistive mode shows a high pressure sensitivity (-6.8 kPa(-1)) and an ultrafast response time (<5 ms), which enables the detection of minute static pressure (0.6 Pa), vibration level (0.1 m s(-2)), and sound pressure (approximate to 57 dB). The flexible e-skin in a piezoelectric mode is also demonstrated to be able to detect fast dynamic stimuli such as high frequency vibrations (approximate to 250 Hz). The flexible e-skins with both piezoresistive and piezoelectric sensing capabilities may find applications requiring both static and dynamic tactile perceptions such as robotic hands for dexterous manipulations and various healthcare monitoring devices.close1