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Bagging is a crucial step in the full cycle management of pear cultivation, with increasing labor costs annually, driving research on intelligent bagging equipment. We describe an intelligent bagging end-effector for pears, which employs the Yolov8 algorithm for fruitlets detection and the Semi-Global Block Matching (SGBM) algorithm to acquire three-dimensional spatial information of the targets. To address the computational limitations of embedded devices in agricultural intelligent equipment, we improve the YOLOv8 model by replacing its neck component with the Asymptotic Feature Pyramid Network (AFPN) and incorporating Context Guided (CG) blocks into the C2f module. These improvements significantly reduce the model’s parameters and size while enhancing detection accuracy. To provide accurate three-dimensional information to the end-effector, we use the SGBM algorithm to obtain depth information of the targets. We constrain the matching process based on the model’s detected candidate bounding boxes and binocular prior knowledge, which improves the matching accuracy of the model while reducing the computational load. Finally, we deploy the model and matching algorithm on a Jetson Nano, which serves as the controller for the bagging end-effector based on the vacuum negative pressure principle. Experimental results show that the improved model achieves a precision of 96%, an increase of 1.4% over YOLOv8, with the size reduced from 6.2M to 5.0M. The depth information accuracy obtained by the algorithm is within 3cm. The improved model, combined with SGBM, runs at 11.5 fps on the Jetson Nano embedded device, meeting real-time performance requirements.

The intelligent harvesting technology for jujube leaf branches presents a novel avenue for enhancing both the quantity and quality of jujube leaf tea, whereas the precise detection technology for jujube leaf branches emerges as a pivotal factor constraining its development. The precise identification and localization of jujube leaf branches using real-time object detection technology are crucial steps toward achieving intelligent harvesting. When integrated into real-world scenarios, issues such as the background noise introduced by tags, occlusions, and variations in jujube leaf morphology constrain the accuracy of detection and the precision of localization. To address these issues, we describe a jujube leaf branch object detection network based on YOLOv7. First, the Polarized Self-Attention module is embedded into the convolutional layer, and the Gather-Excite module is embedded into the concat layer to incorporate spatial information, thus achieving the suppression of irrelevant information such as background noise. Second, we incorporate implicit knowledge into the Efficient Decoupled Head and replace the original detection head, enhancing the network's capability to extract deep features. Third, to address the issue of imbalanced jujube leaf samples, we employ Focal-EIoU as the bounding box loss function to expedite the regression prediction and enhance the localization accuracy of the model's bounding boxes. Experiments show that the precision of our model is 85%, which is increased by 3.5% compared to that of YOLOv7-tiny. The mAP@0.5 value is 83.7%. Our model's recognition rate, recall and mean average precision are superior to those of other models. Our method could provide technical support for yield estimation in the intelligent management of jujube orchards. [ABSTRACT FROM AUTHOR]

Cellular demise is a pivotal event in both developmental processes and disease states, with mitochondrial regulation playing an essential role. Traditionally, cell death was categorized into distinct types, considered to be linear and mutually exclusive pathways. However, the current understanding has evolved to recognize the complex and interconnected mechanisms of cell death, especially within apoptosis, pyroptosis, and necroptosis. Apoptosis, pyroptosis, and necroptosis are governed by intricate molecular pathways, with mitochondria acting as central decision-makers in steering cells towards either apoptosis or pyroptosis through various mediators. The choice between apoptosis and necroptosis is often determined by mitochondrial signaling and is orchestrated by specific proteins. The molecular dialogue and the regulatory influence of mitochondria within these cell death pathways are critical research areas. Comprehending the shared elements and the interplay between these death modalities is crucial for unraveling the complexities of cellular demise. [ABSTRACT FROM AUTHOR]