A lightweight grape detection model in natural environments based on an enhanced YOLOv8 framework.

Grapefruit and stem detection play a crucial role in automated grape harvesting. However, the dense arrangement of fruits in vineyards and the similarity in color between grape stems and branches pose challenges, often leading to missed or false detections in most existing models. Furthermore, these models' substantial parameters and computational demands result in slow detection speeds and difficulty deploying them on mobile devices. Therefore, we propose a lightweight TiGra-YOLOv8 model based on YOLOv8n. Initially, we integrated the Attentional Scale Fusion (ASF) module into the Neck, enhancing the network's ability to extract grape features in dense orchards. Subsequently, we employed Adaptive Training Sample Selection (ATSS) as the label-matching strategy to improve the quality of positive samples and address the challenge of detecting grape stems with similar colors. We then utilized the Weighted Interpolation of Sequential Evidence for Intersection over Union (Wise-IoU) loss function to overcome the limitations of CIoU, which does not consider the geometric attributes of targets, thereby enhancing detection efficiency. Finally, the model's size was reduced through channel pruning. The results indicate that the TiGra-YOLOv8 model's mAP(0.5) increased by 3.33% compared to YOLOv8n, with a 7.49% improvement in detection speed (FPS), a 52.19% reduction in parameter count, and a 51.72% decrease in computational demand, while also reducing the model size by 45.76%. The TiGra-YOLOv8 model not only improves the detection accuracy for dense and challenging targets but also reduces model parameters and speeds up detection, offering significant benefits for grape detection.

Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy.

In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.

YOLO-plum: A high precision and real-time improved algorithm for plum recognition.

Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant challenges to accurate and complete labeling using mainstream algorithms like YOLOv5. In this study, we established the first artificial dataset of plums and used deep learning to improve target detection. Our improved YOLOv5 algorithm achieved more accurate and rapid batch identification of immature plums, resulting in improved quality and economic benefits. The YOLOv5-plum algorithm showed 91.65% recognition accuracy for immature plums after our algorithmic improvements. Currently, the YOLOv5-plum algorithm has demonstrated significant advantages in detecting unripe plums and can potentially be applied to other unripe fruits in the future.