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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.

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]

This article examines the characteristics of a penetrator with a triangular cross-section perpendicular impacting a semi-infinite metal target. Experiments and numerical simulations were conducted at an impact velocity of 1600 to 1800 m/s. The model, which was constructed to analyze penetration depth, showed the effect of the nose-shaped structure of the penetrator on penetration characteristics. The model can analyze the penetration characteristics of a penetrator with triangular cross-section that attacks semi-infinite targets. The results obtained by the model are primarily attributed to the self-sharpening characteristics of the penetrator. [ABSTRACT FROM AUTHOR]

Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing. [ABSTRACT FROM AUTHOR]