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Hibiscus syriacus L. is a renowned ornamental plant. We constructed 95 chloroplast genomes of H. syriacus L. cultivars using a short-read sequencing platform (Illumina) and a long-read sequencing platform (Oxford Nanopore Technology). The following genome assembly, we delineate quadripartite structures encompassing large single-copy, small single-copy, and inverted repeat (IRa and IRb) regions, from 160,231 bp to 161,041 bp. Our comprehensive analyses confirmed the presence of 79 protein-coding genes, 30 tRNA genes, and 4 rRNA genes in the pan-chloroplast genome, consistent with prior research on the H. syriacus chloroplast genome. Subsequent pangenome analysis unveiled widespread genome sequence conservation alongside unique cultivar-specific variant patterns consisting of 193 single-nucleotide polymorphisms and 61 insertions or deletions. The region containing intra-species variant patterns, as identified in this study, has the potential to develop accession-specific molecular markers, enhancing precision in cultivar classification. These findings are anticipated to drive advancements in breeding strategies, augment biodiversity, and unlock the agricultural potential inherent in H. syriacus. [ABSTRACT FROM AUTHOR]

Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, an automated system for disease detecting in crops will play an important role in agriculture by constructing early detection system of disease. To develop this system, construction of a stepwise disease detection model using images of diseased-healthy plant pairs and a CNN algorithm consisting of five pre-trained models. The disease detection model consists of three step classification models, crop classification, disease detection, and disease classification. The ‘unknown’ is added into categories to generalize the model for wide application. In the validation test, the disease detection model classified crops and disease types with high accuracy (97.09%). The low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model. Our model has the potential to apply to smart farming of Solanaceae crops and will be widely used by adding more various crops as training dataset. [ABSTRACT FROM AUTHOR]