Improving crop yield potential through an enhanced response to rising atmospheric CO2 levels is an effective strategy for sustainable crop production in the face of climate change. Large-sized panicles (containing many spikelets per panicle) have been a recent ideal plant architecture (IPA) for high-yield rice breeding. However, few breeding programs have proposed an IPA under the projected climate change. Here, we demonstrate through the cloning of the rice (Oryza sativa) quantitative trait locus for MORE PANICLES 3 (MP3) that the improvement in panicle number increases grain yield at elevated atmospheric CO2 levels. MP3 is a natural allele of OsTB1/FC1, previously reported as a negative regulator of tiller bud outgrowth. The temperate japonica allele advanced the developmental process in axillary buds, moderately promoted tillering, and increased the panicle number without negative effects on the panicle size or culm thickness in a high-yielding indica cultivar with large-sized panicles. The MP3 allele, containing three exonic polymorphisms, was observed in most accessions in the temperate japonica subgroups but was rarely observed in the indica subgroup. No selective sweep at MP3 in either the temperate japonica or indica subgroups suggested that MP3 has not been involved and utilized in artificial selection during domestication or breeding. A free-air CO2 enrichment experiment revealed a clear increase of grain yield associated with the temperate japonica allele at elevated atmospheric CO2 levels. Our findings show that the moderately increased panicle number combined with large-sized panicles using MP3 could be a novel IPA and contribute to an increase in rice production under climate change with rising atmospheric CO2 levels.

イネ科植物に代表される穀粒を利用する作物にとって，穂の形態は収量に直結する重要な形質である．一つの穂は茎の先端に存在するシュート頂メリステムから形成され，その形態は，シュート頂メリステムにおける発生プログラムの転換とシュート頂メリステムから生み出される側生器官の原基の発生運命により決定される．これらの過程では，シュート頂メリステムや側生器官の原基を構成する個々の細胞の形態と配置の時系列変化と，それらがもたらす細胞間相互作用が重要である．3Dイメージングは組織構造を維持したまま細胞の動態変化が解析できる有力な手法の一つだが，シロイヌナズナを除く多くの植物では適切なイメージング解析技術の不足から報告は少なく，特に単子葉植物における解析例は極めて少ない．オオムギ（Hordeum vulgare）はイネ科に属する主要な穀物のひとつで，その穂の形成過程は「シュート頂メリステムの機能転換と消失」，「ユニークな特徴をもつ側生器官の原基の形成と消失」によって成り立ち，ダイナミックな形態変化が伴う．近年の植物組織の透明化技術 (Kurihara and Mizuta 2017) の開発により，オオムギの幼穂の形成過程の観察が可能となってきており，本稿では，オオムギの幼穂形成過程の概要とイメージング手法について紹介する．

In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.

Agronomically important traits often develop during the later stages of crop growth as consequences of various plant-environment interactions. Therefore, the temporal physiological states that change and accumulate during the crop's life course can significantly affect the eventual phenotypic differences in agronomic traits among crop varieties. Thus, to improve productivity, it is important to elucidate the associations between temporal physiological responses during the growth of different crop varieties and their agronomic traits. However, data representing the dynamics and diversity of physiological states in plants grown under field conditions are sparse. In this study, we quantified the endogenous levels of five phytohormones - auxin, cytokinins (CKs), ABA, jasmonate and salicylic acid - in the leaves of eight diverse barley (Hordeum vulgare) accessions grown under field conditions sampled weekly over their life course to assess the ongoing fluctuations in hormone levels in the different accessions under field growth conditions. Notably, we observed enormous changes over time in the development-related plant hormones, such as auxin and CKs. Using 3' RNA-seq-based transcriptome data from the same samples, we investigated the expression of barley genes orthologous to known hormone-related genes of Arabidopsis throughout the life course. These data illustrated the dynamics and diversity of the physiological states of these field-grown barley accessions. Together, our findings provide new insights into plant-environment interactions, highlighting that there is cultivar diversity in physiological responses during growth under field conditions.

Incorporating deep learning in the image analysis pipeline has opened the possibility of introducing precision phenotyping in the field of agriculture. However, to train the neural network, a sufficient amount of training data must be prepared, which requires a time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of , where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. After training with such a dataset, performance based on recall and the average Precision of the real-world test dataset achieved 96% and 95%, respectively. Applying our pipeline enables extraction of morphological parameters at a large scale, enabling precise characterization of the natural variation of barley from a multivariate perspective. Importantly, we show that our approach is effective not only for barley seeds but also for various crops including rice, lettuce, oat, and wheat, and thus supporting the fact that the performance benefits of this technique is generic. We propose that constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs needed to prepare the training dataset for deep learning in the agricultural domain.

A new series of salicyl glycoconjugates containing hydrazide and hydrazone moieties were designed and synthesized. The bioassay indicated that the novel compounds had no in vitro fungicidal activity but showed significant in vivo antifungal activity against the tested fungal pathogens. Some compounds even had superior activity than the commercial fungicides in greenhouse trial. The results of RT-PCR analysis showed that the designed salicyl glycoconjugates could induce the expression of LOX1 and Cs-AOS2, which are the specific marker genes of jasmonate signaling pathway, to trigger the plant defense resistance.