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With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality.

The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and lighting. In this study, an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting. We applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses. Further, we improved the deep learning network YOLO-TGI by incorporating Ghost and CBAM modules, which was trained and tested in conjunction with premier lightweight detection models like YOLOX and NanoDet in evaluating leaf health conditions. For the cascading with various base detectors, we integrated state-of-the-art trackers such as Byte-Track, Motpy, and FairMot to enable fruit counting in video streams. Experimental results indicated that the combination of YOLO-TGI and Byte-Track achieved the most robust performance. Particularly, YOLO-TGI-N emerged as the model with the least computational demands, registering the lowest FLOPs at 2.05 G and checkpoint weights at 3.7 M, while still maintaining a mAP of 0.72 for leaf disease detection. Regarding the fruit counting, the combination of YOLO-TGI-S and Byte-Track achieved the best R2 of 0.93 and the lowest RMSE of 9.17, boasting an inference speed that doubles that of the YOLOX series, and is 2.5 times faster than the NanoDet series. The developed network framework is a potential solution for researchers facilitating the deployment of similar surveillance models for a broad spectrum of fruit and vegetable crops.

Background: Increasing global overweight and obesity rates not only increase the prevalence of myocardial infarction (MI), but also exacerbate ischemic injury and result in worsened prognosis. Currently, there are no drugs that can reverse myocardial damage once MI has occurred, therefore discovering drugs that can potentially limit the extent of ischemic damage to the myocardium is critical. Resveratrol is a polyphenol known for its antioxidant properties, however whether prolonged daily intake of resveratrol during obesity can protect against MI-induced damage remains unexplored. Methods: We established murine models of obesity via high-fat/high-fructose diet, along with daily administrations of resveratrol or vehicle, then performed surgical MI to examine the effects and mechanisms of resveratrol in protecting against myocardial ischemic injury. Results: Daily administration of resveratrol in obese mice robustly protected against myocardial ischemic injury and improved post-MI cardiac function. Resveratrol strongly inhibited oxidative and DNA damage via activating SIRT3/FOXO3a-dependent antioxidant enzymes following MI, which were completely prevented upon administration of 3-TYP, a selective SIRT3 inhibitor. Hence, the cardioprotective effects of prolonged resveratrol intake in protecting obese mice against myocardial ischemic injury was due to reestablishment of intracellular redox homeostasis through activation of SIRT3/FOXO3a signaling pathway. Conclusion: Our findings provide important new evidence that supports the daily intake of resveratrol, especially in those overweight or obese, which can robustly decrease the extent of ischemic damage following MI. Our study therefore provides new mechanistic insight and suggests the therapeutic potential of resveratrol as an invaluable drug in the treatment of ischemic heart diseases.

The clinical outcome of spinal fusion surgery is closely related to the success of bone fusion. Nowadays, the interbody cage which is used to replace the disc for spinal fusion is expected to have biological activity to improve osseointegration, especially for the aging and osteoporotic patients. Here, through micro-arc oxidation and hydrothermal treatment (MAO + HT), a bioactive CaP coating with micro/nano multilevel morphology was developed on 3D printed Ti6Al4V alloy then verified in vitro and in sheep anterior cervical decompression fusion model systematically. In vitro studies have confirmed the positive effects of characteristic micro/nano morphology and hydrophilicity of the coating formed after surface treatment on the adhesion, proliferation, and osteogenic differentiation of osteoblast precursor cells. Furthermore, the MAO + HT treated interbody cage showed a closer integration with the surrounding bone tissue, improved kinetic stability of the implanted segment, and significantly reduced incidence of fusion failure during the early postoperative period, which indicated that such a surface modification strategy is applicable to the biomechanical and biological microenvironment of the intervertebral space., Competing Interests: The patent for the surface treatment technology used in the study was vested in Beijing AKec Medical Co., Ltd. The authors Chong-Bin Wei and Hao Wang work for the company. No financial support for the study was provided by the company. No other author has reported a potential conflict of interest relevant to this article., (This journal is © The Royal Society of Chemistry.)