Authors

Abstract

To address the limitation of existing agricultural unmanned plant protection equipment in perceiving crop growth status in real time during the maize seedling stage, this study proposes a crop row extraction method based on image processing. A crop semantic segmentation network was developed using the UNet framework, with VGG19 as the encoder and transposed convolution as the decoder. Model testing demonstrated that the segmentation network achieved accuracy rates of 0.9865 on the training set and 0.9864 on the validation set, with corresponding loss values of 0.0254 and 0.0270. In continuous processing scenarios, the average time for semantic segmentation per image was 120 milliseconds, while crop row extraction required 23 milliseconds.

Abstract in Chinese

为解决现有农用无人植保机具在玉米苗期作业时无法现场感知作物生长状况的问题，提出一种基于图像处理的玉米苗期作物行提取方法。基于Unet框架构建了作物语义分割网络，编码器为VGG19，解码器为转置卷积，通过添加注意力机制来增强主要特征信息的权重。采用传统图像处理技术获取作物的轮廓、最小外接圆的圆心与半径等关键信息，引入数据缓存机制，增加用于作物行提取的作物数据量。运用苗带自动聚类算法，在作物行数量与位置不确定的情况下，自动对作物行的数量及其所包含的作物对象进行聚类。模型测试结果表明：作物语义分割模型在验证集与测试集上的准确率分别达到0.9865和0.9864，损失率分别为0.0254和0.0270。在实际连续处理过程中，单张图片的语义分割时间平均为120毫秒，作物行提取时间为23毫秒。