Premise: X‐ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers.Methods and Results: We present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single‐leaf microCT scans into detailed segmentations. By training the model on each scan using six hand‐segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation.Conclusions: Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high‐throughput plant phenotyping.

More than half of global water use can be attributed to crop irrigation, and as the human population grows, so will the water requirements of agriculture. Improved irrigation will be critical to mitigating the impact of increased requirements. An ideal irrigation system is informed by measurements of water demand—a combination of water use and water status signals—and delivers water to plants based on this demand. In this review, examples of methods for monitoring water status are reviewed, along with details on stem and trunk water potential measurements. Then, methods for monitoring evapotranspiration (ET), or water use, are described. These methods are broken into coarse- and fine-scale categories, with a 10 m spatial resolution threshold between them. Fourteen crop ET technologies are presented, including examples of a successful estimation of ET in research and field settings, as well as limitations. The focus then shifts to water distribution technologies, with an emphasis on the challenges associated with the development of systems that achieve dynamic single plant resolution. Some attention is given to the process of choosing ET and water status sensing methods as well as water delivery system design given site characteristics and agronomic goals. This review concludes with a short discussion on the future directions of ET research and the importance of translating findings into useful tools for growers.

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.

Premise of the study: X-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organisation. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small datasets, restricting its utility for phenotyping experiments and limiting our confidence in the conclusion of these studies due to low replication numbers. Methods and Results: We present a Python codebase for random-forest machine learning segmentation and 3D leaf anatomical traits quantification which dramatically reduces the time required to process leaf microCT scans. By training the model on 6 hand segmented image slices out of >1500 in the full dataset, it achieves >90% accuracy in background and tissue segmentation, including veins and bundle sheaths grouped together, but not when taken separately. Conclusion: Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.