使用精准农业的农业深度学习框架改善作物生产.
J Logeshwaran1, Durgesh Srivastava2, K Sree Kumar3
1Department of Computer Science, Christ University, Bengaluru, Karnataka, 560029, India.
BMC bioinformatics
|November 2, 2024
概括
农业深度学习框架 (ADLF) 使用人工智能来改善精准农业的作物管理. 这种人工智能框架增强了决策,导致更好的作物产量和减少损失.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
背景情况:
- 精准农业通过监测和调整作物生长因素来优化农业.
- 人工智能 (AI),特别是深度学习,为精准农业提供了显著的好处.
- 农业深度学习框架 (ADLF) 旨在利用大数据集解决关键作物种植挑战.
研究的目的:
- 通过深度学习技术提高精准农业的有效性.
- 处理包括土壤水分,温度和湿度在内的大量数据集,用于作物行为预测.
- 改善决策,提前发现作物问题,提高农业生产力.
主要方法:
- 农业深度学习框架 (ADLF) 的开发和应用.
- 使用深度学习模型来分析农业数据集.
- 处理变量,如土壤水分,温度和湿度.
主要成果:
- 该ADLF实现了85.41%的准确性,84.87%的精度,84.24%的回忆,以及88.91%的F1-Score.
- 在作物管理方面表现出强大的预测能力,错误率低.
- 表示显著提高决策,作物产量和减少农业损失.
结论:
- ADLF通过为作物管理提供洞察力,显著改善了精准农业.
- 能够提前发现问题,优化资源使用,提高作物产量.
- 人工智能驱动的农业显示出革命农业,提高效率和可持续性的潜力.
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