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As the source of data acquisition, sensors provide basic data support for crop planting decision management and play a foundational role in developing smart planting. Accurate, stable, and deployable on-site sensors make intelligent monitoring of various planting scenarios possible. Recent breakthroughs in plant advanced sensors and the rapid development of intelligent manufacturing and artificial intelligence (AI) have driven sensors towards miniaturization, intelligence, and multi-modality.

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Nitrogen fertilizer application is an important method for the production of high-quality maize. However, nitrogen fertilizer addition patterns vary according to regional climate, field management practices, and soil conditions. In this study, a meta-analysis was used to quantify the yield effects of nitrogen addition on maize, and meta-regression analysis and a random forest model were used to study the main factors affecting the yield effects of nitrogen addition on maize.

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Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production.

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In recent years, advancements in modern laboratory diagnostics have identified () as the major cause of osteoarticular infections in early childhood. The introduction of novel diagnostic methods has ushered in a new era, transitioning from underrated infections to recognizing as the primary etiology of skeletal system infections in children. This article provides a new perspective on , exploring innovative diagnostic methods that have improved and will continue to transform the management of these infections.

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