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Review Open access Jul 2026

A Systematic Review of Dynamic Disease Phenotyping in Plant Pathology

Plant disease phenotyping underpins resistance breeding, epidemiology and crop-loss management, yet it remains a recognised bottleneck. This review asked whether the two metrics that dominate the discipline, the disease severity index (DSI) and the area under the disease progress curve (AUDPC), adequately represent disease as a temporally unfolding process, and what the evidence says about dynamic alternatives. Reporting followed PRISMA 2020 and the Synthesis Without Meta-analysis (SWiM) guideline. Web of Science Core Collection, Scopus, PubMed and a Google Scholar grey-literature sweep were searched for records published between January 2020 and December 2025, retrieving 1,192 records; 874 remained after de-duplication, 128 full texts were assessed and 31 studies met the eligibility criteria. Citation chasing added 24 foundational works, giving 55 included studies. Records were dual-screened (Cohen's kappa = 0.86), appraised with an adapted Mixed Methods Appraisal Tool, and synthesised using vote counting by direction of effect, an evidence map and structured cross-study comparison; meta-analysis was inappropriate because outcomes were not commensurable. Thirty studies (54.5%) represented disease at a single assessment and eight (14.5%) collapsed the epidemic into one integrated area, whereas only twelve (21.8%) retained the full trajectory. Across six outcome domains, all 29 study-level comparisons favoured the temporally richer method and none reported a null or negative result, an asymmetry indicating probable reporting bias. Certainty was high for visual-assessment findings, moderate for sensing and dynamic modelling, and low for field-realised genetic gain. The phenotyping bottleneck has migrated from data acquisition to data representation.

Yusha'u El-Sunais, A. A. Bem, D. Musa · 0 citations
Review Open access Jul 2026

OPTIMIZING eDNA METABARCODING IN INDONESIAN FRESHWATERS: A SCOPING REVIEW OF BEST PRACTICES

ARTICLE HIGLIGHTS- Environmental DNA studies in Indonesian freshwaters remain uneven.- Multi-step filtering improves species detection and reduces false findings in tropical condition.- Taxa-specific primers detect native species better than broad-range primers.- Local databases and manual plausibility checks reduce wrong species matches.ABSTRACTIndonesian freshwater ecosystems harbor immense biodiversity yet remain understudied due to logistical constraints of conventional methods. Environmental DNA (eDNA) metabarcoding offers a noninvasive, high-resolution alternative for biodiversity monitoring, but its uptake in Indonesia is still nascent and methodologically heterogeneous. We reviewed 106 peer-reviewed studies (of which 28 studies were eligible) published between 2015 and 2025 across Google Scholar, PubMed, DOAJ, and GARUDA, charting sampling designs, molecular workflows, and bioinformatics pipelines. Studies were heavily skewed toward Java and West Sumatra (86%) and overwhelmingly employed filtration-based sampling. Broad-range COI and 12S markers dominated (39% and 36% of studies, respectively), whereas fish-specific MiFish-U primers, demonstrating superior sensitivity and specificity, were only used in 14% of cases. Correspondingly, non-specific primer in shotgun metagenomics proven imprudent. Studies using 0.22 µm filters collected a lot of species as much as non-target amplification, while 0.45 µm filters performed inconsistently. Bioinformatics approaches (QIIME2, DADA2, mBRAVE) differed widely but showed no clear impact on detection outcomes. Key limitations included geographic and taxonomic biases, poor reference library coverage, and a lack of expert-driven plausibility checks. We recommend a standardized workflow combining coarse pre-filtration, taxa-specific primers (e.g., MiFish for fish), and manual validation against expanded local databases to strengthen eDNA-based biodiversity assessments in Indonesia.

I. D. A. P. Dwipayana, N. Febryanti, Yan Ramona · 0 citations
Review Open access Aug 2026

CRISPR and Artificial Intelligence in Crop Improvement: A Critical Synthesis for Precision Plant Breeding

Clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing and artificial intelligence (AI) are increasingly presented as a unified route to precision plant breeding. Their convergence is scientifically plausible but unevenly demonstrated. CRISPR systems can create targeted sequence changes, whereas AI can prioritise candidate genes, integrate genomic and phenomic data, optimise guide RNAs and editors, predict editing outcomes, and support iterative genotype-to-phenotype learning. This critical narrative review evaluates the evidence linking these capabilities across the crop-improvement pipeline. Literature published from 1 January 2012 to 5 June 2026 was selected through transparent searches of accessible scholarly indexes and bibliographic resources, followed by citation tracking, metadata verification and thematic appraisal. Evidence is strongest for CRISPR-mediated improvement of discrete, biologically well-characterised traits, including disease resistance, quality attributes, plant architecture and selected stress responses. AI has also achieved useful performance in phenotyping, genomic prediction and CRISPR design, but superiority over conventional statistical or rule-based approaches is not consistent across datasets, species or prediction tasks. Direct evidence for fully integrated, AI-guided CRISPR breeding programmes that deliver stable field performance remains limited. Major constraints include uncertain causal target identification, small and non-representative training datasets, poor transferability across genetic backgrounds, polyploidy, genotype-by-environment interaction, transformation and regeneration bottlenecks, incomplete detection of unintended outcomes, and heterogeneous regulation. The most defensible interpretation is therefore that AI and CRISPR are complementary components of an emerging design-build-test-learn framework rather than a mature autonomous breeding platform. Progress will depend on plant-specific benchmark datasets, prospective validation, multi-environment field trials, interoperable data standards, equitable access to transformation and computational infrastructure, and governance focused on the properties and evidence of resulting products. Their integration can accelerate precision breeding, but biological causality, experimental validation and breeding judgement remain indispensable.

Anilkumar Lalasing Chavan, Pavan Rathod G. P., Chandana Suresh K. S. et al. · 0 citations
Open access Jul 2026

A high-quality chromosome-level genome assembly and annotation of the medicinal orchid Cremastra appendiculata (Orchidaceae).

Cremastra appendiculata (D. Don) Makino, an endangered terrestrial orchid with significant medicinal value, lacks comprehensive genomic resources to elucidate its evolution and conservation genetics. Here, we present a chromosome-level genome assembly for C. appendiculata, generated by integrating PacBio circular consensus sequencing and Hi-C technologies. The final genome assembly totals 2.40 Gb, achieving contig and scaffold N50 values of 27.47 Mb and 98.06 Mb, respectively. Hi-C scaffolding anchored 98.66% of the assembly to 24 chromosomes, with repetitive elements comprising 83.85% of the genome and a total of 31,703 protein-coding genes predicted, of which 29,094 (91.77%) were functionally annotated. This high-quality, chromosome-level reference genome provides a foundational resource for understanding the population genetic structure, adaptive evolution and speciation mechanisms of C. appendiculata, thereby offering valuable insights into its evolutionary history and conservation.

Yongchao Tang, B. Xiao, Ruimin Yu et al. · 0 citations
#gene editing Review Open access Aug 2026

CRISPR-Cas9 Precision Breeding for Climate-resilient Vegetable Crops: A Critical Appraisal of Evidence, Inference and Deployment

Confidence in current claims of climate resilience remains low for most targets, and progress will depend on multi-environment field testing, yield-based endpoints, and extension of editing capability beyond the few genotypes that presently regenerate reliably.

M. Kharat, Vaibhav U. Bansod, Nisha R. Thorat et al. · 0 citations
Open access Jul 2026

High-quality draft genome assembly and functional annotation of Musa textilis cv. Inosa

Introduction Abaca (Musa textilis Née) is an important fiber crop cultivated primarily in the Philippines and valued for its exceptional fiber strength and industrial applications. Despite its economic importance, genomic resources for abaca remain limited, constraining efforts in molecular breeding and trait improvement. Here, we present a high-quality de novo genome assembly and functional annotation of M. textilis cv. Inosa, a commercially important cultivar known for superior fiber quality. Methods The genome was sequenced using PacBio HiFi technology and assembled de novo, followed by repeat annotation, gene prediction, functional characterization, and comparative genomic analyses with other Musa genomes. Orthology, synteny, and fiber-related gene analyses were performed to investigate genome evolution and identify genes associated with fiber development. Results The assembled genome spans 612.5 Mb across 388 contigs, with a contig N50 of 9.02 Mb and a BUSCO completeness score of 98.9%, indicating high assembly quality and completeness. Functional annotation identified 37,403 high-confidence protein-coding genes. Repetitive elements account for 59.18% of the genome, representing one of the highest repeat contents reported among Musa genomes. Notably, Polinton transposons, a rarely reported transposable element class in Musa, were identified. Comparative genomic analyses revealed strong macrosyntenic conservation with other M. textilis assemblies and identified 226 Inosa-specific orthogroups. In addition, 348 proteins associated with fiber biosynthesis were annotated, including key enzymes and regulatory proteins involved in cellulose and lignin biosynthesis pathways. Discussion This high-quality genome assembly expands the genomic resources available for abaca and provides insights into genome organization, repeat landscape, and fiber-related gene content. The genome will support comparative genomics, marker development, and breeding strategies aimed at improving fiber quality, disease resistance, and climate resilience in abaca.

Roneil Christian S. Alonday, Julianne Vilela, Damsel C. Bangcal-Villariño et al. · 0 citations