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  • 1Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

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此摘要是机器生成的。

这项研究介绍了一种改进的辐射基神经网络算法 (IM-RBNNA),用于精确的榴莲受精. IM-RBNNA准确地预测了土壤的营养含量和产量,优化了肥料计划,以增加收获和降低成本.

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榴莲种植的时间榴莲精确施肥精确的受精榴莲土壤营养管理榴莲产量预测预测精确的营养供应提供精确的营养.

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科学领域:

  • 农业科学 农业科学
  • 人工智能的人工智能
  • 土壤科学 土壤科学

背景情况:

  • 榴莲种植需要精确的土壤营养管理,以获得最佳产量.
  • 了解土壤营养素 (N,P,K) 与榴莲产量之间的关系对于有效的肥策略至关重要.

研究的目的:

  • 开发和评估一个改进的辐射基神经网络算法 (IM-RBNNA),用于精确的榴莲受精.
  • 为了提高土壤营养含量的预测准确性及其与榴莲产量的相关性.

主要方法:

  • 提出了一种改进的辐射基础神经网络算法 (IM-RBNNA),结合灰狼算法来优化权重和值.
  • 收集了土壤营养和历史产量数据,以训练和验证IM-RBNNA模型.
  • 将IM-RBNNA的性能与其他相关算法进行了比较.

主要成果:

  • 在预测土壤N,K和P含量方面,IM-RBNNA表现优于其他三种算法,其平均相对误差和平均绝对误差较低,确定系数更高.
  • 该算法准确地预测了土壤营养素和榴产量之间的复杂关系.

结论:

  • IM-RBNNA算法为榴莲土壤营养含量和产量提供了准确的预测,帮助农民制定有效的农学计划.
  • 通过IM-RBNNA高效地利用营养资源,最大限度地减少环境影响,最大限度地提高榴莲生长潜力,降低成本,提高整体产量.