在伊利诺伊州使用水产作物,半物理模型和人工神经网络评估作物产量预测模型
Vishal Gautam1, Abdul Gani2, Shray Pathak1
1Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India.
Scientific reports
|July 29, 2025
概括
准确的作物产量估计对农业至关重要. 这项研究发现,人工神经网络 (ANN) 模型在预测伊利诺伊州的玉米和大豆产量方面优越,优于其他方法.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 准确的作物产量估计对于农业生产率和经济稳定至关重要.
- 美国伊利诺伊州的玉米和大豆是主要作物,对地区产量产生重大影响.
- 传统的产量估计方法在有效管理农业数据方面面临挑战.
研究的目的:
- 评估和比较人工神经网络 (ANN),半物理和AquaCrop模型的性能,以估计玉米和大豆产量.
- 为了确定在伊利诺伊州作物产量预测最准确的建模方法.
- 利用气象和土地表面数据进行增强的农业预测.
主要方法:
- 将AquaCrop模型与人工神经网络 (ANN) 和半物理模型集成.
- 利用了25年的气象数据 (降雨量,温度,湿度,风速,太阳辐射) 和来自NASA POWER,USDA和NASS的陆地表面水指数.
- 收集的玉米和大豆收益率数据分别为每公7.06至14.66和每公2.49至4.37.
主要成果:
- 在玉米和大豆产量方面,ANN模型显示出最高的预测准确度,在大豆方面达到0.96的确定系数 (R2).
- 使用ANN预测的玉米产量在6.81至15.63/公之间,而大豆产量在2.45至4.43/公之间.
- 半物理模型显示了大豆产量预测的最低R2值 (0.42),表明与ANN相比,预测能力较低.
结论:
- 人工神经网络 (ANN) 模型对于准确的作物产量估计非常有效,特别是对于玉米和大豆.
- 这些发现强调了ANN模型在与伊利诺伊州相似的环境条件的地区用于农业预测的重要性.
- 这项研究为利用先进的建模技术改善农业管理和经济规划提供了坚实的框架.
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