Minji Oh

Digital phenotyping is increasingly recognized as an essential tool for the quantitative analysis of fungal morphology, particularly in controlled indoor cultivation systems where large numbers of fruiting bodies must be assessed consistently and non-destructively. While YOLOv8-based deep learning approaches have previously been applied in phenotypic analyses of edible mushrooms, the applicability of newer YOLO architectures to fungal phenotyping remains largely unexplored. In this study, we present a controlled-environment digital phenotyping framework for indoor mushroom cultivation and conduct a systematic benchmarking evaluation of YOLOv11 for phenotypic segmentation in comparison with YOLOv8. Using bottle-cultivated Pleurotus ostreatus and Flammulina velutipes as representative edible basidiomycetes, we performed a controlled comparison of YOLOv8-seg and YOLOv11-seg using identical datasets, preprocessing pipelines, and hyperparameter configurations. The results demonstrate that YOLOv11 achieves segmentation performance comparable to that of YOLOv8 across all evaluated metrics (ΔmAP_50–95 < 0.01) while substantially reducing computational complexity, including fewer trainable parameters, lower FLOPs, and decreased gradient load. Validation against caliper-based physical measurements revealed moderate, trait-dependent agreement, whereas inter-model consistency between YOLOv8 and YOLOv11 remained consistently high across diverse morphological and segmentation scenarios. These findings suggest that recent developments in object detection architectures can improve computational efficiency without compromising phenotypic measurement fidelity. More broadly, this study highlights the importance of periodically evaluating emerging detection architectures within biological phenotyping pipelines to ensure scalable, sustainable, and high-throughput fungal phenotyping under controlled-environment cultivation systems.

Digital phenotyping is increasingly recognized as a critical tool for quantitative analysis of fungal morphology, particularly in controlled indoor cultivation systems where large numbers of fruiting bodies must be assessed consistently and non-destructively. While YOLOv8-based deep learning approaches have been previously applied to phenotypic analysis of edible mushrooms, the applicability of newer YOLO architectures to fungal phenotyping remains unexplored. In this study, we present a controlled-environment digital phenotyping framework for indoor mushroom cultivation and evaluate the feasibility of YOLOv11 for phenotypic analysis through a direct comparison with YOLOv8. Using bottle-cultivated Pleurotus ostreatus and Flammulina velutipes as representative edible basidiomycetes, we conducted a systematic comparison of YOLOv8-seg and YOLOv11-seg under identical datasets, preprocessing pipelines, and hyperparameter configurations. The results demonstrate that YOLOv11 achieves segmentation performance comparable to YOLOv8 across all evaluated metrics (ΔmAP50–95 < 0.01), while substantially reducing computational complexity, including fewer trainable parameters, lower FLOPs, and decreased gradient load. Validation against caliper-based physical measurements revealed moderate, trait-dependent agreement, whereas inter-model consistency between YOLOv8 and YOLOv11 was consistently high across diverse morphological and segmentation scenarios. These findings indicate that architectural refinements in YOLOv11 primarily enhance computational efficiency and training behavior without altering phenotypic interpretation. Collectively, this study provides the first validation of YOLOv11 for mushroom phenotyping and highlights its potential as an efficient analytical tool for future high-throughput fungal morphology studies.

This study aimed to develop and evaluate new hybrid strains of Pleurotus cystidiosus with enhanced functional and physiological characteristics. Hybridization between the parental strains KMCC01257 and KMCC05164 yielded four promising hybrid lines (PA-054, PA-104, PA-122, and PA-132), which were selected based on superior mycelial growth and yield performance. Morphological traits and productivity were evaluated across three developmental stages: primordium formation (C1), fruiting body development (C2), and maturation (C3). As cultivation progressed, the number of fruiting bodies decreased, whereas total yield per cultivation bag increased, indicating that late-stage management plays a critical role in maximizing productivity. Chemical analyses revealed that water extracts contained higher levels of polysaccharides, ergothioneine, and total phenolics than EtOAc extracts. Among the tested strains, PA-132 exhibited the highest phenolic content and strongest antioxidant activity, while PA-104 showed greater polysaccharide and ergothioneine accumulation than the parental strains. Antioxidant activity increased over developmental stages and was consistently higher in water extracts, whereas α-glucosidase inhibitory activity was detected primarily in EtOAc extracts with minimal variation among strains or stages. Overall, the results demonstrate that hybridization effectively enhanced the biosynthesis of bioactive metabolites and the functional properties of P. cystidiosus. The selected hybrid strains, particularly PA-132 and PA-054, represent promising candidates for the development of high-value functional mushroom cultivars and nutraceutical applications.

Mushrooms have long been economically and nutritionally important crops, and recent advances in digital agriculture have increased interest in automating phenotypic evaluation. Due to the limitation of traditional phenotype assessment, various artificial intelligence (AI) models including YOLOv8 have been introduced to evaluate mushroom phenotypes non-destructively and efficiently. However, unlike previous models, few studies of mushroom phenotype assessment with YOLOv11 were published. In this study, using Pleurotus ostreatus and Flammulina velutipes, comparison of mushroom phenotype analysis between YOLOv8 and YOLOv11 was processed. All images were captured under controlled conditions and conducted to be preprocessed for the model evaluation. The results demonstrated that YOLOv11 achieved segmentation accuracy comparable to YOLOv8 (ΔmAP50–95 < 0.01) while substantially improving computational efficiency with a reduction of approximately 15–20%. In validation with the physical measurements of mushroom phenotype, both models showed biologically meaningful and moderate correlations across phenotypic traits (r ≈ 0.2–0.44; R² ≈ 0.72–0.83), confirming that YOLO-derived measurements captured essential dimensional variation. Inter-model comparisons revealed strong consistency (r ≥ 0.94, R² ≥ 0.96, MAE ≤ 0.40), indicating that YOLOv11 maintained the predictive reliability of YOLOv8 while operating with superior computational efficiency. This study establishes YOLOv11 as a robust foundation for AI-assisted digital breeding and automated quality monitoring systems in fungal research and precision agriculture.

Gene editing using CRISPR/Cas9 is an innovative tool for developing new mushroom strains, offering a promising alternative to traditional breeding methods that are time-consuming and labor-intensive. However, plasmid-based gene editing presents several challenges, including the need for selecting appropriate promoters for Cas9 expression, optimizing codons for the Cas9 gene, the unintended insertion of fragmented plasmid DNA into genomic DNA (gDNA), and regulatory concerns related to genetically modified organisms (GMOs). To address these issues, we utilized a Ribonucleoprotein (RNP) complex consisting of Cas9 and gRNA for gene editing to modify the A mating-type gene of Lentinula edodes. To overcome the challenges posed by the large size of the Cas9 protein, which limits its penetration through the protoplast membrane, and the susceptibility of sgRNA to degradation, we developed a nanoparticle complex using calcium phosphate and polyacrylic acid. This approach significantly improved gene editing efficiency. Consequently, we successfully edited the mating-controlling genes hd1 and hd2 in L. edodes and examined the effects of their disruption on mating. Disruption of the hd1 gene, which is known to influence mycelial growth, did not significantly affect growth or mating. In contrast, editing the hd2 gene disrupted mating with compatible partners, highlighting its critical role in the mating process. The RNP-based transformation technology presented here offers significant advancement over traditional plasmid-based methods, enhancing the efficiency of targeted gene modification while avoiding the insertion of foreign genetic material, thereby mitigating GMO-related regulatory concerns.

Tricholoma matsutake is a valuable edible mushroom in Eastern Asia. Due to the challenges of artificial cultivation, T. matsutake cultivation has relied on foraging from pine forests. Changes in global climate variables, such as temperature and precipitation, could affect the amount of foraged T. matsutake. In this study, we investigated the correlation between the amount of foraged T. matsutake and climatic factors (average monthly temperature and precipitation) in Yangyang-gun using the augmented Dickey–Fuller test and the ordinary least squares method. Among the assessed correlations between the foraged T. matsutake and different climatic factors, the average temperature in August was significantly correlated with the amount of foraged T. matsutake, increasing by 1.5 tons when the temperature in August increased by 1 unit. Overall, this study identified a potentially strong positive correlation between the average temperature in August and amount of foraged T. matsutake.

The oyster mushroom is cultivated globally, renowned for its unique texture and umami flavor, as well as its rich content of nutrients and functional ingredients. This study aims to identify the descriptive sensory characteristics, assess the consumer acceptability of new superior lines and cultivars of yellow oyster mushrooms, in addition to exploring the relationship between these descriptive characteristics and consumer acceptability. Statistical analyses were performed using one-way analysis of variance (ANOVA), principal component analysis (PCA), and partial least squares regression (PLSR). Twenty attributes were delineated, including three related to appearance/color (gray, yellow, and white), four associated with the smell/odor of fresh mushroom (oyster mushroom, woody, fishy, and seafood smells), three pertaining to the smell/odor of cooked mushrooms (mushroom, umami, and savory smells), four describing flavor/taste (sweet, salty, umami, and savory tastes), and five for texture/mouthfeel (chewy, smooth, hard, squishy, and slippery textures). Consumer acceptability tests involved 100 consumers who evaluated overall liking, appearance, overall taste, sweetness, texture, savory taste, MSG taste, smell, color, purchase intention, and recommendation. The general oyster mushroom (548 samples) scored highest in acceptability. Seven attributes, namely fresh mushroom smell, seafood smell (fresh), fishy smell (fresh), umami smell (cooked), nutty smell (cooked), salty taste, and MSG taste with the exception of appearance showed significant differences among samples (p < 0.001). The three yellow oyster mushroom samples were strongly associated with attributes like hardness, softness (texture), sweet taste (745 samples), MSG taste, salty taste, squishy texture, and fishy smell (483 and 629 samples). The development of sensory lexicons and increasing consumer acceptance of new superior lines and cultivars of yellow oyster mushroom will likely enhance sensory quality and expand the consumer market, aligning with consumer needs and preferences.

Light plays vital roles in fungal growth, development, reproduction, and pigmentation. In Flammulina velutipes, the color of the fruiting body exhibits distinct changes in response to light; however, the underlying molecular mechanisms remain unknown. Therefore, in this study, we aimed to analyze the F. velutipes transcriptome under red, green, and blue light-emitting diode (LED) lights to identify the key genes affecting the light response and fruiting body color in this fungus. Additionally, we conducted protein–protein interaction (PPI) network analysis of the previously reported fruiting body color-related gene, Fvpal1, to identify the hub genes. Phenotypic analysis revealed that fruiting bodies exposed to green and blue lights were darker than those untreated or exposed to red light, with the color intensifying more after 48 h of exposure to blue light compared to that after 24 h of exposure. Differentially expressed gene (DEG) analyses of all light treatments for 24 h revealed that the numbers of DEGs were 17, 74, and 257 under red, green, and blue lights, respectively. Subsequently, functional enrichment analysis was conducted of the DEGs identified under green and blue lights, which influenced the color of F. velutipes. In total, 103 of 168 downregulated DEGs under blue and green lights were included in the enrichment analysis. Among the DEGs enriched under both green and blue light treatments, four genes were related to monooxygenases, with three genes annotated as cytochrome P450s that are crucial for various metabolic processes in fungi. PPI network analysis of Fvpal1 revealed associations with 11 genes, among which the expression of one gene, pyridoxal-dependent decarboxylase, was upregulated in F. velutipes exposed to blue light. These findings contribute to our understanding of the molecular mechanisms involved in the fruiting body color changes in response to light and offer potential molecular markers for further exploration of light-mediated regulatory pathways.

White strains of Hypsizygus marmoreus are more difficult to cultivate than are brown strains; therefore, new white strain breeding strategies are required. Accordingly, we constructed the genetic map of H. marmoreus with 1996 SNP markers on 11 linkage groups (LGs) spanning 1380.49 cM. Prior to analysis, 82 backcrossed strains (HM8 lines) were generated by mating between KMCC03106-31 and the progenies of the F₁ hybrid (Hami-18 × KMCC03106-93). Using HM8, the first 23 quantitative trait loci (QTLs) of yield-related traits were detected with high limit of detection (LOD) scores (1.98–9.86). The length, thickness, and hardness of the stipe were colocated on LG 1. Especially, length of stipe and thickness of stipe were highly correlated given that the correlation coefficients were negative (−0.39, p value ≤ .01). And a typical biomodal distribution was observed for lightness of the pileus and the lightness of the pileus trait belonged to the LG 8, as did traits of earliness and mycelial growth in potato dextrose agar (PDA) medium. Therefore, results for color traits can be suggested that color is controlled by a multi-gene of one locus. The yield trait was highly negatively correlated with the traits for thickness of the stipe (−0.45, p value ≤ .01). Based on additive effects, the white strain was confirmed as recessive; however, traits of mycelial growth, lightness, and quality were inherited by backcrossed HM8 lines. This new genetic map, finely mapped QTLs, and the strong selection markers could be used in molecular breeding of H. marmoreus.

Agaricus bisporus, commonly known as the button mushroom, is widely cultivated throughout the world. To breed new strains with more desirable traits and improved adaptability, diverse germplasm, including wild accessions, is a valuable genetic resource. To better understand the genetic diversity available in A. bisporus and identify previously unknown diversity within accessions, a phylogenetic analysis of 360 Agaricus spp. accessions using single-nucleotide polymorphism genotyping was performed. Genetic relationships were compared using principal coordinate analysis (PCoA) among accessions with known origins and accessions with limited collection data. The accessions clustered into four groups based on the PCoA with regard to genetic relationships. A subset of 67 strains, which comprised a core collection where repetitive and uninformative accessions were not included, clustered into 7 groups following analysis. Two of the 170 accessions with limited collection data were identified as wild germplasm. The core collection allowed for the accurate analysis of A. bisporus genetic relationships, and accessions with an unknown pedigree were effectively grouped, allowing for origin identification, by PCoA analysis in this study.