果园的预测框架产生了多谱遥感和地面特征
1College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.
Plants (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了使用多谱遥感和地面特征预测果产量的新框架. 该模型准确地预测了各种规模的果产量,改进了传统方法.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 传统的果产量估计依赖于手动方法,缺乏效率和多源数据集成.
- 准确的产量预测对于农业规划和市场稳定至关重要.
研究的目的:
- 为果园产量预测开发一个新的框架,整合多谱遥感和地面特征.
- 为了能够在地区/县和小规模果园层面进行产量预测.
- 通过将遥感与地面真相数据相结合,提高果产量估计的准确性.
主要方法:
- 使用现场采集的样本提取果园种植面积.
- 使用通过谷歌地球引擎和APYieldNet模型处理的多光谱和环境特征进行大规模收益预测.
- 使用果实检测 (YOLO-A模型) 和比例校正方法进行小规模果园产量校正.
主要成果:
- 拟议的APYieldNet模型在大规模收益率预测方面取得了卓越的表现 (MAE = 152.68 kg/mu,RMSE = 203.92 kg/mu).
- 在复杂的果园环境中,YOLO-A模型在检测果果和果花中表现出高准确度.
- 比例校正显著提高了小规模果园产量预测的准确性,使它们更接近实际产量.
结论:
- 综合框架有效地预测了多个规模的果产量,优于现有方法.
- 将遥感数据与地面果实检测相结合,可以提高预测的准确性.
- 这种方法为传统的手工产量估计技术提供了更有效,更准确的替代方案.
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