Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and case study on stomatal patterning.

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

Formation of nanochannels in sapphire with ultrashort Bessel pulses.

We explore, both by numerical simulations and experimentally, the flexibility in controlling Bessel beam parameters by re-imaging it into transparent material with a demagnifying collimator for the formation of high-aspect ratio nanochannels. Analysis of nanochannels produced by in-house precision-made axicon with 275 fs pulses in sapphire reveals the intensity threshold of ∼7.2 × 1013 W/cm2 required to create the cylindrical microexplosion. We estimate that the maximum applied pressure during the process was 1.5 TPa and that the resulting density of compressed sapphire in the nanochannel's shells are ∼1.19 ± 0.02 times higher than the pristine crystal, and higher than what was achieved before in spherical microexplosion with Gaussian pulses.

Reforestation as a novel abatement and compliance measure for ground-level ozone.

High ambient ozone (O3) concentrations are a widespread and persistent problem globally. Although studies have documented the role of forests in removing O3 and one of its precursors, nitrogen dioxide (NO2), the cost effectiveness of using peri-urban reforestation for O3 abatement purposes has not been examined. We develop a methodology that uses available air quality and meteorological data and simplified forest structure growth-mortality and dry deposition models to assess the performance of reforestation for O3 precursor abatement. We apply this methodology to identify the cost-effective design for a hypothetical 405-ha, peri-urban reforestation project in the Houston-Galveston-Brazoria O3 nonattainment area in Texas. The project would remove an estimated 310 tons of (t) O3 and 58 t NO2 total over 30 y. Given its location in a nitrogen oxide (NOx)-limited area, and using the range of Houston area O3 production efficiencies to convert forest O3 removal to its NOx equivalent, this is equivalent to 127-209 t of the regulated NOx. The cost of reforestation per ton of NOx abated compares favorably to that of additional conventional controls if no land costs are incurred, especially if carbon offsets are generated. Purchasing agricultural lands for reforestation removes this cost advantage, but this problem could be overcome through cost-share opportunities that exist due to the public and conservation benefits of reforestation. Our findings suggest that peri-urban reforestation should be considered in O3 control efforts in Houston, other US nonattainment areas, and areas with O3 pollution problems in other countries, wherever O3 formation is predominantly NOx limited.

The role of a peri-urban forest on air quality improvement in the Mexico City megalopolis.

Air quality improvement by a forested, peri-urban national park was quantified by combining the Urban Forest Effects (UFORE) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. We estimated the ecosystem-level annual pollution removal function of the park's trees, shrub and grasses using pollution concentration data for carbon monoxide (CO), ozone (O(3)), and particulate matter less than 10 microns in diameter (PM(10)), modeled meteorological and pollution variables, and measured forest structure data. Ecosystem-level O(3) and CO removal and formation were also analyzed for a representative month. Total annual air quality improvement of the park's vegetation was approximately 0.02% for CO, 1% for O(3,) and 2% for PM(10), of the annual concentrations for these three pollutants. Results can be used to understand the air quality regulation ecosystem services of peri-urban forests and regional dynamics of air pollution emissions from major urban areas.