Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

Summary: Rhizoctonia solani is a soil‐borne fungus causing sheath blight. In consistent with its necrotrophic life style, no rice cultivars fully resistant to R. solani are known, and agrochemical plant defense activators used for rice blast, which upregulate a phytohormonal salicylic acid (SA)‐dependent pathway, are ineffective towards this pathogen. As a result of the unavailability of genetics, the infection process of R. solani remains unclear. We used the model monocotyledonous plants Brachypodium distachyon and rice, and evaluated the effects of phytohormone‐induced resistance to R. solani by pharmacological, genetic and microscopic approaches to understand fungal pathogenicity. Pretreatment with SA, but not with plant defense activators used in agriculture, can unexpectedly induce sheath blight resistance in plants. SA treatment inhibits the advancement of R. solani to the point in the infection process in which fungal biomass shows remarkable expansion and specific infection machinery is developed. The involvement of SA in R. solani resistance is demonstrated by SA‐deficient NahG transgenic rice and the sheath blight‐resistant B. distachyon accessions, Bd3‐1 and Gaz‐4, which activate SA‐dependent signaling on inoculation. Our findings suggest a hemi‐biotrophic nature of R. solani, which can be targeted by SA‐dependent plant immunity. Furthermore, B. distachyon provides a genetic resource that can confer disease resistance against R. solani to plants.

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.

Next-generation sequencing (NGS) technologies have enabled genome re-sequencing for exploring genome-wide polymorphisms among individuals, as well as targeted re-sequencing for the rapid and simultaneous detection of polymorphisms in genes associated with various biological functions. Therefore, a simple and robust method for targeted re-sequencing should facilitate genotyping in a wide range of biological fields. In this study, we developed a simple, custom, targeted re-sequencing method, designated “multiplex PCR targeted amplicon sequencing (MTA-seq),” and applied it to the genotyping of the model grass Brachypodium distachyon. To assess the practical usability of MTA-seq, we applied it to the genotyping of genome-wide single-nucleotide polymorphisms (SNPs) identified in natural accessions (Bd1-1, Bd3-1, Bd21-3, Bd30-1, Koz-1, Koz-3, and Koz-4) by comparing the re-sequencing data with that of reference accession Bd21. Examination of SNP-genotyping accuracy in 443 amplicons from eight parental accessions and an F₁ progeny derived by crossing of Bd21 and Bd3-1 revealed that ~95% of the SNPs were correctly called. The assessment suggested that the method provided an efficient framework for accurate and robust SNP genotyping. The method described here enables easy design of custom target SNP-marker panels in various organisms, facilitating a wide range of high-throughput genetic applications, such as genetic mapping, population analysis, molecular breeding, and genomic diagnostics.

Clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR‐associated nuclease 9 (Cas9) system‐based genome editing has been applied to a wide range of species, including economically important agricultural crops (Doudna and Charpentier 2014). Genome editing by the direct delivery of ribonucleoproteins (RNPs) offers various advantages compared to the conventional transgene‐based method, including high efficiency, low time requirements, reduced off‐target effects, and low cytotoxicity (Jeon et al. 2017; Spicer and Molnar 2018).

Summary: Phosphate (Pi) deficiency is known to be a major limitation for symbiotic nitrogen fixation (SNF), and hence legume crop productivity globally. However, very little information is available on the adaptive mechanisms, particularly in the important legume crop chickpea (Cicer arietinum L.), which enable nodules to respond to low‐Pi availability. Thus, to elucidate these mechanisms in chickpea nodules at molecular level, we used an RNA sequencing approach to investigate transcriptomes of the nodules in Mesorhizobium mediterraneum SWRI9–(MmSWRI9)–chickpea and M. ciceri CP‐31–(McCP‐31)–chickpea associations under Pi‐sufficient and Pi‐deficient conditions, of which the McCP‐31–chickpea association has a better SNF capacity than the MmSWRI9–chickpea association during Pi starvation. Our investigation revealed that more genes showed altered expression patterns in MmSWRI9‐induced nodules than in McCP‐31‐induced nodules (540 vs. 225) under Pi deficiency, suggesting that the Pi‐starvation‐more‐sensitive MmSWRI9‐induced nodules required expression change in a larger number of genes to cope with low‐Pi stress than the Pi‐starvation‐less‐sensitive McCP‐31‐induced nodules. The functional classification of differentially expressed genes (DEGs) was examined to gain an understanding of how chickpea nodules respond to Pi starvation, caused by soil Pi deficiency. As a result, more DEGs involved in nodulation, detoxification, nutrient/ion transport, transcriptional factors, key metabolic pathways, Pi remobilization and signalling were found in Pi‐starved MmSWRI9‐induced nodules than in Pi‐starved McCP‐31‐induced nodules. Our findings have enabled the identification of molecular processes that play important roles in the acclimation of nodules to Pi deficiency, ultimately leading to the development of Pi‐efficient chickpea symbiotic associations suitable for Pi‐deficient soils.