实时玉米产量监测系统的研究与基于DNN的预测模型
Chaojie Yin1, Qi Zhang1, Xu Mao1,2
1College of Engineering, China Agricultural University, Beijing, China.
Frontiers in plant science
|September 11, 2024
概括
本研究介绍了一种先进的实时玉米产量监测系统,使用机器学习来克服当前方法的局限性. 深度神经网络模型实现了高精度,提高了精确农业决策.
科学领域:
- 农业工程 农业工程
- 精准农业 精准农业 精准农业
- 机器学习应用 机器学习应用
背景情况:
- 目前基于光电传感器的玉米产量监测有准确性和稳定性问题,原因是忽视了玉米堆叠变化.
- 诸如养量,地形和速度等因素显著影响玉米堆叠模式,影响产量预测的准确性.
研究的目的:
- 开发一个强大的实时玉米产量监测系统,用于收割机.
- 通过解决堆叠模式变化,提高玉米产量预测的准确性和可靠性.
主要方法:
- 使用EDEM离散元件模拟来分析不同料量下玉米堆叠模式.
- 在自主开发的双电梯测试装置上开发了一个实时监控系统.
- 使用复合数据集训练和验证深度神经网络 (DNN),梯度增强机 (GBM) 和随机森林 (RF) 模型.
主要成果:
- 在实验室测试中,DNN模型的R2为0.998,RMSE为0.526和MAE为0.425,在实验室测试中表现出卓越的性能.
- 实地实验证实了DNN模型的有效性,实现了2.29%的相对误差和97.85%的平均准确性.
- 拟议的系统和DNN模型显示了对集成收割机应用的高精度和可靠性.
结论:
- 开发的实时监测系统有效地解决了传统产量监测方法的局限性.
- 机器学习,特别是DNN,在准确和稳定的玉米产量预测方面取得了重大进展.
- 该系统通过提供可靠的实时产量数据来改善管理决策,从而增强精准农业.
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