High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addressed. Advances in sensor technology have led to the increasing use of imaging to monitor and measure a range of plants including the model Arabidopsis thaliana. These extensive datasets contain diverse trait information, but feature extraction is often still implemented using approaches requiring substantial manual input. The computational detection and segmentation of individual fruits from images is a challenging task, for which we have developed DeepPod, a patch-based 2-phase deep learning framework. The associated manual annotation task is simple and cost-effective without the need for detailed segmentation or bounding boxes. Convolutional neural networks (CNNs) are used for classifying different parts of the plant inflorescence, including the tip, base, and body of the siliques and the stem inflorescence. In a post-processing step, different parts of the same silique are joined together for silique detection and localization, whilst taking into account possible overlapping among the siliques. The proposed framework is further validated on a separate test dataset of 2,408 images. Comparisons of the CNN-based prediction with manual counting (R² = 0.90) showed the desired capability of methods for estimating silique number. The DeepPod framework provides a rapid and accurate estimate of fruit number in a model system widely used by biologists to investigate many fundemental processes underlying growth and reproduction

Five genes of large phenotypic effect known to confer abiotic stress tolerance in rice were selected to characterize allelic variation in commercial Colombian tropical japonica upland rice cultivars adapted to drought‐prone acid soil environments (cv. Llanura11 and Porvenir12). Allelic variants of the genes ART1, DRO1, SUB1A, PSTOL1, and SPDT were characterized by PCR and/or Sanger sequencing in the two upland cultivars and compared with the Nipponbare and other reference genomes. Two genes were identified as possible targets for gene editing: SUB1A (Submergence 1A), to improve tolerance to flooding, and SPDT (SULTR3;4) (SULTR‐like Phosphorus Distribution Transporter), to improve phosphorus utilization efficiency and grain quality. Based on technical and regulatory considerations, SPDT was targeted for editing. The two upland cultivars were shown to carry the SPDT wild‐type (nondesirable) allele based on sequencing, RNA expression, and phenotypic evaluations under hydroponic and greenhouse conditions. A gene deletion was designed using the CRISPR/Cas9 system, and specialized reagents were developed for SPDT editing, including vectors targeting the gene and a protoplast transfection transient assay. The desired edits were confirmed in protoplasts and serve as the basis for ongoing plant transformation experiments aiming to improve the P‐use efficiency of upland rice grown in acidic soils.

Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often labour-intensive, time-consuming, and prone to human error. However, its precision and adaptability in accurately phenotyping organ-level traits, such as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effectiveness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using images from a multiparent advanced generation intercross (MAGIC) mapping family. We trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong performance with an average precision of 88.0% for detection and 55.9% for segmentation. Quantitative trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on automated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scalable and provides high-quality phenotype data, facilitating genetic analysis and gene discovery, as well as advancing crop breeding research.

Vascular wilt, caused by the pathogenFusarium oxysporumf. sp.physali(Foph), is a major disease of cape gooseberry (Physalis peruvianaL.) in Andean countries. Despite the economic losses caused by this disease, there are few studies related to molecular mechanisms in theP. peruviana—Fophpathosystem as a useful tool for crop improvement. This study evaluates eight candidate genes associated with this pathosystem, using real-time quantitative PCR (RT-qPCR). The genes were identified and selected from 1,653 differentially expressed genes (DEGs) derived from RNA-Seq analysis and from a previous genome-wide association study (GWAS) of this plant-pathogen interaction. Based on the RT-qPCR analysis, the tubuline (TUB) reference gene was selected for its highly stable expression in cape gooseberry. The RT-qPCR validation of the candidate genes revealed the biological variation in their expression according to their known biological function. Three genes related to the first line of resistance/defense responses were highly expressed earlier during infection in a susceptible genotype, while three others were overexpressed later, mostly in the tolerant genotype. These genes are mainly involved in signaling pathways after pathogen recognition, mediated by hormones such as ethylene and salicylic acid. This study provided the first insight to uncover the molecular mechanism from theP. peruviana—Fophpathosystem. The genes validated here have important implications in the disease progress and allow a better understanding of the defense response in cape gooseberry at the molecular level. Derived molecular markers from these genes could facilitate the identification of tolerant/susceptible genotypes for use in breeding schemes.

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.