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In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning.

As cropland and water resources are most valuable, the rational development and utilization of agricultural water and land resources are crucial for national food, ecological, and resource security. Therefore, obtaining timely and accurate information about the quantity and utilization of these resources is vital for national stability and socio-economic development. Here, we summarize the status quo and problems of the monitoring and information services of agricultural water and land resources in China, and expound on the framework of a monitoring and information service system. Additionally, we propose the strategic goals, namely, realizing automatic, intelligent, and unmanned monitoring of agricultural resources and environment and marketing the monitoring information services. These goals can be achieved by increasing the efficiency and outcomes of water and land use and optimizing the spatial allocation of agricultural land and water resources. Moreover, we analyze the technological routes for agricultural water and land resource monitoring in the context of the deep integration of information technology and agriculture, and propose specific projects from the aspects of infrastructure construction, core technology development and integration, and information services. Furthermore, we propose the following policy suggestions: (1) strengthening the top-level design, (2) enhancing scientific and technological innovation, (2) building a sharing platform for agricultural resource monitoring, (4) deepening the interaction of government, industry, universities, research institutes, and users of agricultural resources, and (5) promoting high-level talent training. Our study is expected to provide theoretical references for the development of the agricultural water and land resource monitoring in China.

As the global fruit growing area continues to increase and the population aging problem continues to intensify, fruit and vegetable production is constrained by the difficulties of labor shortages and high costs. Single-arm harvesting robots are inefficient, and in order to balance harvesting accuracy and efficiency, research on multi-arm harvesting robots has become a hot topic. This paper summarizes the performance of multi-arm harvesting robots in indoor and outdoor environments from the aspects of automatic navigation technology, fruit and vegetable identification and localization, multi-arm workspace optimization, and multi-arm harvesting task planning and analyzes their advantages and challenges in practical applications. The results show that the lack of application of automatic field navigation for multi-arm harvesting robots, the low harvesting rate in non-structured environments, and the complexity of algorithms for multi-arm harvesting robots' task planning are the main challenges hindering their wide-scale application. Future studies need to focus on building a standardized growing environment to control the amount of information acquired by the robots and optimize the multi-arm control strategy of these challenges, which is an important direction for research on multi-arm harvesting robots. [ABSTRACT FROM AUTHOR]

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards. [ABSTRACT FROM AUTHOR]

Background: Hyperactivation of CD4+ T cells in peripheral blood and airway tissues has been suggested to play a key role in the development and maintenance of chronic inflammation in childhood asthma. However, the underlying mechanisms are not yet clear., Objective: To investigate alterations in serum levels of T helper cell-related cytokines, mitogen-stimulated CD4+ T cell proliferation and activation-induced cell death (AICD) in childhood asthma., Methods: 21 children with untreated asthma and 21 healthy volunteers (age and gender matched) participated in this study. Th1/Th2/Th17 cytokines in serum were analyzed by flow cytometry. CD4+ T cells were isolated from participants by using immuno-magnetic beads and were stimulated by phytohemagglutinin (PHA). Cell proliferation was evaluated with a Cell Counting Kit-8 (CCK-8). Activation induced cell death (AICD) of CD4+ T cells was also induced by PHA and apoptosis was assayed by annexin V/PI staining. Quantitative RT-PCR was carried out to analyze Fas and FasL mRNA expression. FLIPL, caspase-8 and Bcl-2 were detected by western blot analysis., Results: In children with asthma, the proliferative capacity of CD4+ T cells was enhanced and AICD was inhibited significantly, while serum IL-4, IL-10 and TNF were markedly higher compared with the control group. Fas mRNA expression in the asthma group was obviously lower than that in the control group, while no change was detected in FasL mRNA expression. Western blot analysis showed that the levels of the anti-apoptotic proteins, FLIPL and Bcl-2 in CD4+ T cells of the asthma group were significantly higher than in the control group. Spearman's correlation tests showed that only IL-4 correlated positively with FLIPL and Bcl-2 expression, while IL-10 and TNF were unrelated to FLIPL and Bcl-2 expression., Conclusions: These results indicate that enhanced proliferation and defective AICD of CD4+ T cells influence the T cell-mediated inflammatory reaction in childhood asthma and that increased IL-4, FLIPL and Bcl-2 expression and decreased Fas expression jointly participate in these changes in cell proliferation and apoptosis.ression and decreased Fas expression jointly participate in these changes in cell proliferation and apoptosis.