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Updated: Jun 18, 2025

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物联网用于智能灌控制和作物推,使用农业4.0应用中的交互式指南深度模型
Smita Sandeep Mane1,2, Vaibhav E Narawade1
1Department of Computer Engineering, Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, Navi Mumbai, India.
概括
农业4.0通过持续的土壤监测和作物建议来提高作物产量. 一个新的交互指导优化器-深度卷积神经网络 (交互指导优化器-DCNN) 模型在土壤参数预测和作物适宜性评估方面实现了高准确性.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 农业4.0允许持续监测土壤,以提高作物产量和肥力管理.
- 关键的土壤参数如pH,,,和水分对于灌和作物选择至关重要.
研究的目的:
- 开发一个先进的智能灌控制模型,并根据土壤肥沃度准确的作物建议.
- 通过一种新的深度学习方法,增强有关土壤营养的决策.
主要方法:
- 利用交互指南优化器-深度卷积神经网络 (交互指南优化器-DCNN) 进行智能灌和作物推.
- 实现了一种以自然为灵感的交互指南优化器,取代了ADAM算法,以数据下采样提高效率.
- 用于模型评估的k倍交叉验证 (k=10).
主要成果:
- 该模型在预测土壤矿物质,pH值和水分方面取得了93.11%的准确性.
- 在90%的模型训练下,获得了97.12%的作物推准确率.
- 该模型在矿物质预测方面表现出强的F-score (90.30%),特异性 (92.12%),灵敏度 (89.56%) 和准确性 (86.36%).
结论:
- 开发的交互指南优化器-DCNN模型有效地支持农业4.0.0中的智能灌和作物推系统.
- 该模型在土壤参数预测方面的高准确性和有效性突显了其优化农业实践的潜力.
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