在智能农业中使用多式联络数据的CNN应用的审查.
Mohammad El Sakka1,2, Mihai Ivanovici3, Lotfi Chaari1,4
1Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
卷积神经网络 (CNN) 通过增强杂草和疾病检测,作物分类和产量预测来彻底改变智能农业. 这些人工智能工具对于可持续农业和满足全球粮食安全需求至关重要.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 智能农业集成技术以优化农场效率,与农业5.0原则保持一致.
- 卷积神经网络 (CNN) 对于分析农业数据越来越重要.
- 现有的文献评论为理解农业中CNN应用提供了基础.
研究的目的:
- 审查和分析卷积神经网络 (CNN) 在智能农业中的应用.
- 在农业5.0.0框架内将CNN置于背景中.
- 确定当前的进展,方法和未来的研究方向,用于农业CNN.
主要方法:
- 关于CNN在农业中的应用的115多项最近研究的综合文献综述.
- 对更广泛的研究环境进行图书统计分析.
- 对CNN方法的分析,包括图像分类,细分,回归和对象检测.
- 评估各种数据类型 (RGB,多光谱,雷达,热) 和数据源 (无人机,卫星).
主要成果:
- CNNs在杂草检测,疾病检测,作物分类,水资源管理和产量预测方面取得了重大进展.
- 无人机和卫星数据,使实时,大规模的作物监测能够实现先进的农场管理.
- 对比分析表明,CNN在处理高维或时间农业数据方面优于传统的机器学习和其他深度学习模型.
结论:
- 卷积神经网络 (CNN) 是可持续农业的关键工具,应对气候变化和粮食安全等挑战.
- 未来的研究应该专注于整合物联网,云平台和大型语言模型,以增强数据处理和监管洞察力.
- 提高数据可访问性和开发混合模型是推动农业CNN应用的关键.
相关概念视频
02:16Light Acquisition
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.
02:35Key Elements for Plant Nutrition
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 atmosphere, the...