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相关概念视频

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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Key Elements for Plant Nutrition

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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
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相关实验视频

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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在精密农业中使用低成本的图像数据采集来识别害虫.

S Hemalatha1, M Sangeetha2

  • 1Sri Shakthi Institute of Engineering and Technology, Department of Computer Science and Engineering, Coimbatore, India.

Brazilian journal of biology = Revista brasleira de biologia
|May 15, 2024
PubMed
概括

无人驾驶飞行器 (UAV) 或无人机通过整合大数据和深度学习来加强精准农业,以保护作物. 这项技术有助于识别植物疾病并优化农业实践,以便更好地做出决策.

科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 无人驾驶飞行器 (UAV) 越来越多地被用于精密农业,因为它们的操作效率和广泛适用性.
  • 大数据分析和信息和通信技术 (ICT) 对于在现代农业中提取可操作的见解至关重要.
  • 深度学习显示了农业中数据密集型应用的巨大潜力,包括作物监测和管理.

研究的目的:

  • 研究无人机在精准农业中的应用,以保护作物和检测疾病.
  • 分析用于指挥农业害虫管理中的无人机舰队的通信协议.
  • 评估深度学习模型的有效性,以识别植物疾病.

主要方法:

  • 利用大数据分析来处理农业信息并支持决策.
  • 在作物保护场景中检查了无人机舰队指挥和控制的通信协议.
  • 采用深度学习模型,包括视觉几何组 (VGG-16),卷积神经网络 (CNN) 和完全卷积网络 (FCN),用于植物疾病检测.
  • 应用人工免疫系统 (AIS) 调整深层神经网络以适应动态环境条件.

主要成果:

  • 模拟结果表明,拟议的深度学习方法在检测植物疾病方面取得了卓越的表现.
  • 无人机,大数据和深度学习的综合方法为精准农业提供了增强的能力.

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  • 该研究表明,人工智能驱动的方法在改善作物保护策略方面的有效性.

    • 结论:

    • 无人机技术与大数据和深度学习相结合,代表了精准农业的重大进步.
    • 开发的方法为自动作物监测,疾病识别和害虫管理提供了强大的框架.
    • 这项研究支持更明智,更有效的农业实践,从而提高作物产量和资源管理.