农作物倒伏区域分级识别方法及系统

• Format: Patent
• Language: Chinese
• Published: 28.03.2025
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

本发明公开了一种农作物倒伏区域分级识别方法及系统，包括：获取研究区域农作物倒伏的遥感影像，对其进行预处理后，再对预处理后的遥感影像进行标注制作成数据集；建立两阶段的作物倒伏分级监测模型LHMNet，其包括用于第一阶段倒伏区域识别的LAR-ResUNet模型以及对LAR-ResUNet模型识别的倒伏区域进行第二阶段倒伏严重程度分级的LSC-ResUNet模型；采用数据集训练分别训练作物倒伏分级监测模型LHMNet中的LAR-ResUNet模型和LSC-ResUNet模型，得到训练后的LAR-ResUNet模型和LSC-ResUNet模型，再采用训练后的作物倒伏分级监测模型LHMNet对农作物倒伏区域分级程度进行预测。本发明能够清晰完整地从大区域中提取出倒伏区域的同时，并能缩小不同倒伏严重程度的水稻分割准确性的差异。 The invention discloses a crop lodging region grading identification method and system, and the method comprises the steps: obtaining a remote sensing image of crop lodging in a research region, carrying out the preprocessing of the remote sensing image, and carrying out the marking of the preprocessed remote sensing image, and making a data set; establishing a two-stage crop lodging grading monitoring model LHMNet, wherein the two-stage crop lodging grading monitoring model LHMNet comprises an LAR-ResUNet model used for first-stage lodging region identification and an LSC-ResUNet model used for second-stage lodging severity grading of a lodging region i