Introduction: Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions.Methods: Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation.Results: The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general.Discussion: The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration.

Aroma is among of the most important criteria that indicate the quality of food and beverage products. Aroma compounds can be found as free molecules or glycosides. Notably, a significant portion of aroma precursors accumulates in numerous food products as nonvolatile and flavorless glycoconjugates, termed glycosidic aroma precursors. When subjected to enzymatic hydrolysis, these seemingly inert, nonvolatile glycosides undergo transformation into fragrant volatiles or volatiles that can generate odor-active compounds during food processing. In this context, microbial β-glucosidases play a pivotal role in enhancing or compromising the development of flavors during food and beverage processing. β-glucosidases derived from bacteria and yeast can be utilized to modulate the concentration of particular aroma and taste compounds, such as bitterness, which can be decreased through hydrolysis by glycosidases. Furthermore, oral microbiota can influence flavor perception by releasing volatile compounds that can enhance or alter the perception of food products. In this review, considering the glycosidic flavor precursors present in diverse food and beverage products, we underscore the significance of glycosidases with various origins. Subsequently, we delve into emerging insights regarding the release of aroma within the human oral cavity due to the activity of oral microbial glycosidases.