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As a crop with significant medicinal value and nutritional components, the market demand for bitter melon continues to grow. The diversity of bitter melon shapes has a direct impact on its market acceptance and consumer preferences, making precise identification of bitter melon germplasm resources crucial for breeding work. To address the limitations of time-consuming and less accurate traditional manual identification methods, there is a need to enhance the automation and intelligence of bitter melon phenotype detection. This study developed a bitter melon phenotype detection model named CSW-YOLO. By incorporating the ConvNeXt V2 module to replace the backbone network of YOLOv8, the model's focus on critical target features is enhanced. Additionally, the SimAM attention mechanism was introduced to compute attention weights for neurons without increasing the parameter count, further enhancing the model's recognition accuracy. Finally, WIoUv3 was introduced as the bounding box loss function to improve the model's convergence speed and positioning capabilities. The model was trained and tested on a bitter melon image dataset, achieving a precision of 94.6%, a recall of 80.6%, a mAP50 of 96.7%, and an F1 score of 87.04%. These results represent improvements of 8.5%, 0.4%, 11.1%, and 4% in precision, recall, mAP50, and F1 score, respectively, over the original YOLOv8 model. Furthermore, the effectiveness of the improvements was validated through heatmap analysis and ablation experiments, demonstrating that the CSW-YOLO model can more accurately focus on target features, reduce false detection rates, and enhance generalization capabilities. Comparative tests with various mainstream deep learning models also proved the superior performance of CSW-YOLO in bitter melon phenotype detection tasks. This research provides an accurate and reliable method for bitter melon phenotype identification and also offers technical support for the visual detection technologies of other agricultural products. [ABSTRACT FROM AUTHOR]

Automatic harvesting robots are crucial for enhancing agricultural productivity, and precise fruit maturity detection is a fundamental and core technology for efficient and accurate harvesting. Strawberries are distributed irregularly, and their images contain a wealth of characteristic information. This characteristic information includes both simple and intuitive features, as well as deeper abstract meanings. These complex features pose significant challenges to robots in determining fruit ripeness. To increase the precision, accuracy, and efficiency of robotic fruit maturity detection methods, a strawberry maturity detection algorithm based on an improved CES-YOLOv8 network structure from YOLOv8 was developed in this study. Initially, to reflect the characteristics of actual planting environments, the study collected image data under various lighting conditions, degrees of occlusion, and angles during the data collection phase. Subsequently, parts of the C2f module in the YOLOv8 model's backbone were replaced with the ConvNeXt V2 module to enhance the capture of features in strawberries of varying ripeness, and the ECA attention mechanism was introduced to further improve feature representation capability. Finally, the angle compensation and distance compensation of the SIoU loss function were employed to enhance the IoU, enabling the rapid localization of the model's prediction boxes. The experimental results show that the improved CES-YOLOv8 model achieves an accuracy, recall rate, mAP50, and F1 score of 88.20%, 89.80%, 92.10%, and 88.99%, respectively, in complex environments, indicating improvements of 4.8%, 2.9%, 2.05%, and 3.88%, respectively, over those of the original YOLOv8 network. This algorithm provides technical support for automated harvesting robots to achieve efficient and precise automated harvesting. Additionally, the algorithm is adaptable and can be extended to other fruit crops. [ABSTRACT FROM AUTHOR]

Purpose: The crucial role of N6-methyladenosine (m6A) methylation in anti-tumor immunity and immunotherapy has been broadly depicted. However, the molecular phenotypic linkages between m6A modification pattern and immunological ecosystem are expected to be disentangled in hepatocellular carcinoma (HCC), for immunotherapeutic unresponsiveness circumvention and combination with promising drug agents. Methods: Modification patterns of m6A methylation were qualitatively dissected according to the large-scale HCC samples profiling. We then determined the immune phenotypic linkages by systematically evaluating their tumor microenvironment composition, immune/stromal-relevant signature, immune checkpoints correlation, and prognostic value. Individual quantification of m6A methylation pattern was achieved by m6Ascore construction, intensified by longitudinal single-cell analysis of immunotherapy cohort and validated by the transcriptomic profiles of our in-hospital GDPH-HCC cohort. Candidate therapeutic agents were also screened out. Results: Three distinct m6A methylation patterns were determined in high accordance with inflamed-, excluded-, and desert-immunophenotype. To be precise, Immune-inflamed high-m6Ascore group was characterized by activated immunity with favorable prognosis. Stromal activation and absence of immune cell infiltration were observed in low-m6Ascore phenotype, linked to impaired outcome. Patients with low-m6Ascore demonstrated diminished responses and clinical benefits for cohorts receiving immunotherapy. The above credible linkage between m6A methylation pattern and tumor immune microenvironment was robustly validated in our GDPH-HCC cohort. Single-cell dynamic change of m6A methylation level in exhausted CD8 T cell and fibroblast was depicted in immunotherapy cohort fore and art. Derived from m6A methylation pattern, seven potential frontline drug agents were recognized as promising choice for high-m6Ascore patients. Conclusion: Our work bridged the credible linkage between epigenetics and anti-tumor immunity in HCC, unraveling m6A modification pattern as immunological indicator and predictor for immunotherapy. Individualized m6Ascore facilitated strategic choices to maximize therapy-responsive possibility. [ABSTRACT FROM AUTHOR]