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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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  1. 首页
  3. 使用多无人机成像和深度学习进行水果检测的智能集成系统.
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  3. 使用多无人机成像和深度学习进行水果检测的智能集成系统.

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使用多无人机成像和深度学习进行水果检测的智能集成系统.

Oleksandr Melnychenko1, Lukasz Scislo2, Oleg Savenko1

  • 1Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts'ka Str., 29016 Khmelnytskyi, Ukraine.

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|March 28, 2024

在PubMed 上查看摘要

概括
此摘要是机器生成的。

这项研究介绍了一种人工智能和深度学习系统,使用多个无人机实时检测和计数果园中的水果. 创新的方法实现了高精度,提高了工业4.0的农业效率.

关键词:
这是YOLOv5的.深度学习是一种深度学习.果实检测检测器 果实检测器果实产量估计结果的估计.同步和自主运动.无人驾驶飞行器是一种无人驾驶飞行器.视频传输流传输流的视频流.

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科学领域:

  • 农业技术 农业技术
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 工业4.0需要通过智能传感器和计算来提高农业效率.
  • 传统的果实检测方法缺乏现代果园管理所需的精度和及时性.
  • 技术整合对于不断发展的农业部门至关重要.

研究的目的:

  • 开发一种新的,由人工智能驱动的系统,用于准确的,实时的水果检测和果园的计数.
  • 利用多个无人机 (UAV) 系统进行同步数据捕获和图像处理.
  • 通过先进的数字农业解决方案,改进果园管理和收获准备.

主要方法:

  • 人工智能 (AI) 和深度学习 (DL) 与多UAV平台的整合.
  • 从多个无人机摄像头同时捕捉和同步视频.
  • 图像质量优化,用于在动态环境中高分辨率的对象检测.

主要成果:

  • 在水果检测和计数方面,平均精度为86.8%.
  • 错误率保持低:14.7%的假阳性和18.3%的假阴性.
  • 在具有挑战性的天气条件下,包括多云的天气条件下,已证明有效.

结论:

  • 多个无人机成像和DL方法提供了卓越的实时水果识别能力.
  • 这项技术代表了数字农业和工业4.0目标的重大进步.
  • 该系统为有效的果园管理和收获计划提供了关键数据.