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Abstract High night air temperature (HNT) stress negatively impacts both rice (Oryza sativaL) yield and grain quality and has been extensively investigated because of the significant yield loss observed (10%) for every increase in air temperature (1°C). Most of the rice HNT studies have been conducted under greenhouse conditions, with limited information on field‐level responses for the major rice sub‐populations. This is due to a lack of a field‐based phenotyping infrastructure that can accommodate a diverse set of accessions representing the wider germplasm and impose growth stage‐specific stress. In this study, we built six high‐tunnel greenhouses and screened 310 rice accessions from the Rice Diversity Panel 1 (RDP1) and 10 commercial hybrid cultivars in a replicated design. Each greenhouse had heating and a cyber–physical system that sensed ambient air temperature and automatically increased night air temperature to about 4°C relative to ambient temperature in the field for two cropping seasons. The system successfully imposed HNT stress of 4.0 and 3.94°C as recorded by Raspberry Pi sensors for 2 weeks in 2019 and 2020, respectively. HOBO sensors (Onset Computer Corporation) recorded a 2.9 and 2.07°C temperature differential of ambient air between control and heated greenhouses in 2019 and 2020, respectively. These greenhouses were able to withstand constant flooding, heavy rains, strong winds (140 mph), and thunderstorms. Selected US rice cultivars showed an average of 24% and 15% yield reduction under HNT during the 2019 and 2020 cropping seasons, respectively. Our study highlights the potential of this computer‐based infrastructure for accurate implementation of HNT or other abiotic stresses under field‐growing conditions. 

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning. 

Summary A higher minimum (night‐time) temperature is considered a greater limiting factor for reduced rice yield than a similar increase in maximum (daytime) temperature. While the physiological impact of high night temperature (HNT) has been studied, the genetic and molecular basis of HNT stress response remains unexplored.We examined the phenotypic variation for mature grain size (length and width) in a diverse set of rice accessions under HNT stress. Genome‐wide association analysis identified several HNT‐specific loci regulating grain size as well as loci that are common for optimal and HNT stress conditions.A novel locus contributing to grain width under HNT conditions colocalized withFie1, a component of the FIS‐PRC2 complex. Our results suggest that the allelic difference controlling grain width under HNT is a result of differential transcript‐level response ofFie1in grains developing under HNT stress.We present evidence to support the role ofFie1in grain size regulation by testing overexpression (OE) and knockout mutants under heat stress. The OE mutants were either unaltered or had a positive impact on mature grain size under HNT, while the knockouts exhibited significant grain size reduction under these conditions.