A prerequisite for solving many agricultural problems is to accurately estimate the area affected by crop disasters and its severity rating. In this letter, we propose a pipeline to segment the drought area and distinguish the severity rating of the maize on RGB images accessed by an unmanned aerial vehicle (UAV) through a semantic segmentation method based on deep learning. First, the ground truth is created through expert evaluation and visual interpretation with the aid of the Normalized Difference Vegetation Index (NDVI). The neural network structure that was used is based on U-Net. Some structural and parameter improvements on U-net were made using SE-ResNeXt-50 as the backbone with the atrous spatial pyramid pooling (ASPP) module. By using RGB images as the input of the neural network for training, the final trained network can work on RGB images captured by a consumer UAV. The experimental results showed that our pipeline achieved an F1-score of 0.9034 and a Jaccard index of 0.8287 on the test set.

中文翻译：

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基于改进U-Net的无人机RGB图像玉米干旱识别与分级

解决许多农业问题的先决条件是准确估计受农作物灾害影响的面积及其严重程度。在这封信中，我们提出了一种通过基于深度学习的语义分割方法来分割干旱区域并区分无人机 (UAV) 访问的 RGB 图像上玉米严重程度等级的管道。首先，在归一化差异植被指数 (NDVI) 的帮助下，通过专家评估和视觉解释创建基本事实。使用的神经网络结构基于 U-Net。使用 SE-ResNeXt-50 作为主干和多孔空间金字塔池 (ASPP) 模块对 U-net 进行了一些结构和参数改进。通过使用RGB图像作为神经网络的输入进行训练，最终训练好的网络可以处理消费者无人机捕获的 RGB 图像。实验结果表明，我们的管道在测试集上实现了 0.9034 的 F1 分数和 0.8287 的 Jaccard 指数。