提高精确农业使用综合生物灵感优化模型作物推在拉贾斯坦邦,印度
Lavika Goel1, Nawdeep Kumar2, Snehal Jain1
1Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan, 302017, India.
Scientific reports
|February 2, 2026
概括
两种新的混合算法,GSA-HGS和EEFO-CPO,使用遥感数据增强人工神经网络 (ANN) 作物推. 这些生物灵感的方法改善了精准农业,为作物季节实现了高分类准确性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 作物推系统对于精准农业至关重要.
- 人工神经网络 (ANN) 是有前途的,但需要高效的优化.
- 遥感数据为农业监测提供了宝贵的见解.
研究的目的:
- 开发和评估基于ANN的作物推的新型混合自然灵感优化算法.
- 利用Landsat 8和9的遥感数据来改进作物分类.
- 提高农业应用中优化算法的性能和稳定性.
主要方法:
- 重力搜索算法 (GSA) 与饥饿游戏搜索 (HGS) 的混合.
- 整合电动鱼食优化 (EEFO) 与山顶猪优化 (CPO).
- 在印度拉贾斯坦邦的Kharif和Rabi季节使用远程传感数据进行作物分类的混合算法的应用.
主要成果:
- GSA-HGS混合实现了95.32% (Kharif) 和94.99% (Rabi) 的分类准确率.
- 这种EEFO-CPO混合实现了94.09% (Kharif) 和94.94% (Rabi) 的分类准确度.
- 两种混合算法都证明了勘探-开发平衡和趋同稳定性的改善.
结论:
- 新的混合优化算法显著提高了作物推的ANN性能.
- 生物灵感策略为精准农业提供了有效的解决方案,特别是在数据稀缺的地区.
- 该研究强调了先进的计算方法在推进农业实践方面的潜力.
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