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相关概念视频

Design Example: Design of an Irrigation Channel01:27

Design Example: Design of an Irrigation Channel

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Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

40
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Adaptations that Reduce Water Loss01:57

Adaptations that Reduce Water Loss

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Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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相关实验视频

Updated: Jun 2, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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使用卷积神经网络 (CNN) 进行果园灌决策.

Atsushi Okayama1, Atsushi Yamamoto1, Masaomi Kimura1

  • 1Department of Environmental Management, Graduate School of Agriculture, Kindai University, Nara, Japan.

Environmental monitoring and assessment
|January 14, 2025
PubMed
概括

这项研究开发了一种智能灌系统,使用卷积神经网络 (CNN) 来监控树. 人工智能模型从叶子图像中准确地检测水压力,有助于在困难的地形中高效地管理果园.

关键词:
自动灌自动化灌图像处理 图像处理机器学习是机器学习.一个人的灵魂.遥感是一种远程传感.水的压力是水的压力.

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科学领域:

  • 农业工程 农业工程
  • 计算机科学 计算机科学
  • 植物科学 植物科学

背景情况:

  • 高效的农业管理,特别是在具有挑战性的地形,是劳动密集型,并依赖于农民的经验.
  • 智能技术为减少农业劳动负担提供了解决方案.
  • 精密灌对于优化作物产量和资源使用至关重要.

研究的目的:

  • 利用卷积神经网络 (CNN) 在果园系统中开发智能灌的决策支持工具.
  • 根据叶子图像评估模型在鉴定树水压力方面的准确性.
  • 评估CNN模型在山区远程灌系统中的潜力.

主要方法:

  • 在两个生长季节收集了土壤湿度数据和叶子RGB图像,用于桃种植.
  • 经过培训,测试和验证CNN模型以确定最佳灌时间.
  • 利用图像分析检测树木中的水应激水平.

主要成果:

  • 美国有线电视新闻网 (CNN) 模型在从皮叶图像中识别水压力水平时取得了超过80%的准确性.
  • 证明了叶子图像分析在自动化灌决策中的潜力.
  • 突出了该模型作为远程灌系统组件的能力.

结论:

  • 开发的CNN模型显示了果园智能灌的前景,特别是在具有挑战性的地形上.
  • 需要进行进一步的研究,以提高模型的稳定性和适应性,以适应各种现场条件.
  • 人工智能驱动的灌管理可以提高农业效率和可持续性.